Vessel size and continuous predictors
An experimental method to deal with continuous predictors and possible alternative functional relationships between them and the trait of interest. In this case, we are modeling vessel size in response to environmental temperature.
## Load in libraries
library(bayou)Loading required package: ape
Loading required package: geiger
Attaching package: ‘geiger’
The following object is masked _by_ ‘.GlobalEnv’:
mkn.lik
Loading required package: phytools
Loading required package: maps
Loading required package: coda
Attaching package: ‘bayou’
The following object is masked from ‘package:grDevices’:
colorRamplibrary(treeplyr)Loading required package: dplyr
Attaching package: ‘dplyr’
The following objects are masked from ‘package:stats’:
filter, lag
The following objects are masked from ‘package:base’:
intersect, setdiff, setequal, union
Attaching package: ‘treeplyr’
The following object is masked from ‘package:stats’:
reorderlibrary(diversitree)
Attaching package: ‘diversitree’
The following object is masked from ‘package:dplyr’:
combine
The following object is masked from ‘package:coda’:
mcmclibrary(optimx)
## Set working directory
setwd("~/repos/growthforms/analysis")td <- readRDS("../output/cleandata/tdOUwie.rds")
td$phy
Phylogenetic tree with 351 tips and 350 internal nodes.
Tip labels:
Tsuga_canadensis, Abies_concolor, Pseudotsuga_menziesii, Picea_smithiana, Pinus_longaeva, Pinus_cembra, ...
Node labels:
Spermatophyta, , Pinales, , , , ...
Rooted; includes branch lengths.
$dat
attr(,"class")
[1] "treedata" "list"
attr(,"tip.label")
[1] "Tsuga_canadensis" "Abies_concolor" "Pseudotsuga_menziesii" "Picea_smithiana"
[5] "Pinus_longaeva" "Pinus_cembra" "Pinus_wallichiana" "Pinus_halepensis"
[9] "Pinus_roxburghii" "Pinus_mugo" "Pinus_sylvestris" "Pinus_contorta"
[13] "Pinus_maximinoi" "Pinus_radiata" "Pinus_patula" "Thuja_occidentalis"
[17] "Juniperus_oxycedrus" "Juniperus_communis" "Prumnopitys_ferruginea" "Prumnopitys_taxifolia"
[21] "Dacrycarpus_dacrydioides" "Podocarpus_totara" "Ascarina_lucida" "Pseudowintera_colorata"
[25] "Hedycarya_arborea" "Eusideroxylon_zwageri" "Beilschmiedia_tawa" "Laurus_azorica"
[29] "Laurus_nobilis" "Litsea_calicaris" "Nectandra_megapotamica" "Ocotea_puberula"
[33] "Virola_surinamensis" "Pycnanthus_angolensis" "Magnolia_grandiflora" "Magnolia_tripetala"
[37] "Magnolia_acuminata" "Buxus_sempervirens" "Platanus_orientalis" "Knightia_excelsa"
[41] "Liquidambar_styraciflua" "Stachyurus_himalaicus" "Terminalia_amazonia" "Terminalia_ivorensis"
[45] "Punica_granatum" "Duabanga_moluccana" "Sonneratia_alba" "Neomyrtus_pedunculata"
[49] "Lophomyrtus_obcordata" "Leptospermum_scoparium" "Kunzea_ericoides" "Eucalyptus_globoidea"
[53] "Eucalyptus_regnans" "Eucalyptus_obliqua" "Eucalyptus_sieberi" "Eucalyptus_grandis"
[57] "Eucalyptus_globulus" "Salvadora_persica" "Capparis_spinosa" "Thymelaea_hirsuta"
[61] "Shorea_leprosula" "Shorea_ovata" "Guazuma_ulmifolia" "Apeiba_tibourbou"
[65] "Apeiba_membranacea" "Grewia_villosa" "Sterculia_apetala" "Cavanillesia_platanifolia"
[69] "Ceiba_pentandra" "Spondias_mombin" "Tapirira_guianensis" "Anacardium_excelsum"
[73] "Mangifera_indica" "Rhus_glabra" "Rhus_coriaria" "Pistacia_lentiscus"
[77] "Pistacia_atlantica" "Billia_hippocastanum" "Acer_cappadocicum" "Harpullia_cupanioides"
[81] "Sapindus_saponaria" "Allophylus_cobbe" "Nephelium_lappaceum" "Litchi_chinensis"
[85] "Alectryon_excelsus" "Swietenia_macrophylla" "Carapa_procera" "Carapa_grandiflora"
[89] "Carapa_guianensis" "Cedrela_odorata" "Cabralea_canjerana" "Simarouba_amara"
[93] "Bulnesia_arborea" "Eucryphia_cordifolia" "Eucryphia_lucida" "Weinmannia_trichosperma"
[97] "Aristotelia_serrata" "Elaeocarpus_hookerianus" "Elaeocarpus_japonicus" "Goupia_glabra"
[101] "Caryocar_glabrum" "Bischofia_javanica" "Hieronyma_alchorneoides" "Erythroxylum_confusum"
[105] "Erythroxylum_macrophyllum" "Erythroxylum_panamense" "Mammea_africana" "Calophyllum_brasiliense"
[109] "Symphonia_globulifera" "Drypetes_lateriflora" "Salix_alba" "Salix_triandra"
[113] "Salix_alaxensis" "Pera_glabrata" "Hevea_brasiliensis" "Gymnanthes_lucida"
[117] "Hura_crepitans" "Euphorbia_tirucalli" "Nothofagus_menziesii" "Nothofagus_fusca"
[121] "Nothofagus_solandri" "Nothofagus_dombeyi" "Fagus_sylvatica" "Quercus_semecarpifolia"
[125] "Quercus_coccifera" "Quercus_faginea" "Castanopsis_indica" "Quercus_ilex"
[129] "Myrica_esculenta" "Juglans_regia" "Juglans_australis" "Casuarina_equisetifolia"
[133] "Casuarina_cunninghamiana" "Carpinus_betulus" "Betula_utilis" "Betula_nana"
[137] "Betula_pubescens" "Betula_pendula" "Krugiodendron_ferreum" "Rhamnus_alaternus"
[141] "Rhamnus_cathartica" "Ziziphus_mistol" "Ziziphus_mucronata" "Ziziphus_jujuba"
[145] "Ziziphus_lotus" "Aphananthe_aspera" "Gironniera_subaequalis" "Trema_cannabina"
[149] "Trema_tomentosa" "Trema_orientalis" "Milicia_excelsa" "Morus_alba"
[153] "Morus_rubra" "Maclura_pomifera" "Maclura_cochinchinensis" "Maclura_tricuspidata"
[157] "Broussonetia_papyrifera" "Brosimum_lactescens" "Brosimum_alicastrum" "Brosimum_guianense"
[161] "Brosimum_utile" "Brosimum_rubescens" "Antiaris_toxicaria" "Maquira_calophylla"
[165] "Pseudolmedia_laevigata" "Perebea_guianensis" "Ficus_carica" "Rubus_swinhoei"
[169] "Rubus_idaeus" "Hagenia_abyssinica" "Sarcopoterium_spinosum" "Polylepis_australis"
[173] "Rosa_canina" "Adenostoma_sparsifolium" "Adenostoma_fasciculatum" "Sorbaria_sorbifolia"
[177] "Holodiscus_discolor" "Spiraea_salicifolia" "Lyonothamnus_floribundus" "Oemleria_cerasiformis"
[181] "Prunus_lusitanica" "Prunus_laurocerasus" "Prunus_serotina" "Prunus_virginiana"
[185] "Prunus_mahaleb" "Prunus_spinulosa" "Prunus_avium" "Prunus_davidiana"
[189] "Prunus_persica" "Prunus_spinosa" "Prunus_cerasifera" "Vauquelinia_californica"
[193] "Sorbus_americana" "Sorbus_aucuparia" "Mespilus_germanica" "Amelanchier_alnifolia"
[197] "Photinia_villosa" "Sorbus_aria" "Malus_pumila" "Malus_baccata"
[201] "Photinia_davidiana" "Pyrus_calleryana" "Rhaphiolepis_indica" "Eriobotrya_japonica"
[205] "Apuleia_leiocarpa" "Castanospermum_australe" "Tetraberlinia_bifoliolata" "Tamarindus_indica"
[209] "Hymenaea_courbaril" "Baikiaea_plurijuga" "Caesalpinia_paraguariensis" "Erythrophleum_ivorense"
[213] "Prosopis_africana" "Acacia_nilotica" "Acacia_tortilis" "Acacia_gerrardii"
[217] "Acacia_karroo" "Acacia_polyacantha" "Acacia_nigrescens" "Acacia_erioloba"
[221] "Cedrelinga_cateniformis" "Albizia_procera" "Pterocarpus_officinalis" "Anagyris_foetida"
[225] "Sophora_microphylla" "Spartium_junceum" "Lecointea_amazonica" "Bocoa_prouacensis"
[229] "Swartzia_polyphylla" "Swartzia_simplex" "Swartzia_arborescens" "Swartzia_panacoco"
[233] "Swartzia_cardiosperma" "Minquartia_guianensis" "Ochanostachys_amentacea" "Alepis_flavida"
[237] "Tamarix_aphylla" "Salsola_vermiculata" "Alangium_chinense" "Alangium_villosum"
[241] "Nyssa_sylvatica" "Nyssa_aquatica" "Norantea_guianensis" "Ficalhoa_laurifolia"
[245] "Eurya_japonica" "Gordonia_lasianthus" "Schima_wallichii" "Camellia_japonica"
[249] "Camellia_sinensis" "Camellia_caudata" "Camellia_brevistyla" "Camellia_oleifera"
[253] "Sideroxylon_obtusifolium" "Manilkara_bidentata" "Symplocos_paniculata" "Symplocos_anomala"
[257] "Symplocos_lucida" "Symplocos_sumuntia" "Symplocos_cochinchinensis" "Symplocos_glomerata"
[261] "Symplocos_tinctoria" "Symplocos_martinicensis" "Bertholletia_excelsa" "Pterostyrax_hispidus"
[265] "Halesia_carolina" "Styrax_camporum" "Styrax_officinalis" "Arbutus_andrachne"
[269] "Rhododendron_anthopogon" "Dracophyllum_strictum" "Sprengelia_incarnata" "Trochocarpa_laurina"
[273] "Monotoca_elliptica" "Oxydendrum_arboreum" "Lyonia_lucida" "Lyonia_ovalifolia"
[277] "Pieris_formosa" "Pieris_japonica" "Gaultheria_shallon" "Gaultheria_mucronata"
[281] "Zenobia_pulverulenta" "Andromeda_polifolia" "Ilex_crenata" "Ilex_dumosa"
[285] "Ilex_decidua" "Ilex_macfadyenii" "Ilex_opaca" "Ilex_aquifolium"
[289] "Ilex_integra" "Ilex_latifolia" "Ilex_canariensis" "Ilex_coriacea"
[293] "Ilex_glabra" "Ilex_chinensis" "Ilex_theezans" "Ilex_guianensis"
[297] "Ilex_anomala" "Ilex_micrococca" "Ilex_rotunda" "Ilex_verticillata"
[301] "Ilex_mitis" "Ilex_goshiensis" "Sambucus_nigra" "Sambucus_racemosa"
[305] "Viburnum_dilatatum" "Viburnum_opulus" "Viburnum_tinus" "Viburnum_odoratissimum"
[309] "Lonicera_maackii" "Lonicera_chrysantha" "Pennantia_corymbosa" "Aralidium_pinnatifidum"
[313] "Griselinia_littoralis" "Schefflera_morototoni" "Hedera_helix" "Schefflera_volkensii"
[317] "Schefflera_digitata" "Carpodetus_serratus" "Garrya_elliptica" "Aucuba_japonica"
[321] "Fagraea_fragrans" "Aspidosperma_quebracho-blanco" "Alstonia_boonei" "Alstonia_scholaris"
[325] "Nerium_oleander" "Uncaria_tomentosa" "Vangueria_infausta" "Genipa_americana"
[329] "Nicotiana_glauca" "Haenianthus_salicifolius" "Olea_capensis" "Olea_europaea"
[333] "Phillyrea_latifolia" "Syringa_reticulata" "Ligustrum_japonicum" "Ligustrum_lucidum"
[337] "Ligustrum_sinense" "Jasminum_fruticans" "Avicennia_marina" "Trichanthera_gigantea"
[341] "Paulownia_tomentosa" "Jacaranda_copaia" "Tecoma_stans" "Spathodea_campanulata"
[345] "Newbouldia_laevis" "Catalpa_bignonioides" "Catalpa_speciosa" "Mansoa_verrucifera"
[349] "Tabebuia_rosea" "Tectona_grandis" "Vitex_agnus-castus"
attr(,"dropped")
attr(,"dropped")$dropped_from_tree
[1] "Myrtus_communis" "Nyctanthes_arbor-tristis" "Paliurus_spina-christi" "Ruyschia_tremadena"
attr(,"dropped")$dropped_from_data
[1] "Blasia_pusilla" "Lunularia_cruciata" "Marchantia_polymorpha" "Riccia_fluitans"
[5] "Reboulia_hemisphaerica" "Marchantia_foliacea" "Conocephalum_conicum" "Haplomitrium_gibbsiae"
[9] "Fossombronia_pusilla" "Pellia_endiviifolia" "Pellia_epiphylla" "Riccardia_multifida"
[13] "Riccardia_latifrons" "Aneura_pinguis" "Aneura_mirabilis" "Metzgeria_conjugata"
[17] "Metzgeria_furcata" "Metzgeria_pubescens" "Metzgeria_temperata" "Porella_pinnata"
[21] "Porella_cordaeana" "Porella_platyphylla" "Porella_obtusata" "Porella_arboris"
[25] "Frullania_dilatata" "Frullania_microphylla" "Frullania_fragilifolia" "Frullania_teneriffae"
[29] "Frullania_tamarisci" "Radula_aquilegia" "Radula_complanata" "Jubula_hutchinsiae"
[33] "Lejeunea_lamacerina" "Lejeunea_cavifolia" "Blepharostoma_trichophyllum" "Herbertus_borealis"
[37] "Lepidozia_reptans" "Kurzia_trichoclados" "Bazzania_tricrenata" "Bazzania_trilobata"
[41] "Lophocolea_heterophylla" "Lophocolea_bidentata" "Chiloscyphus_pallescens" "Pedinophyllum_interruptum"
[45] "Plagiochila_spinulosa" "Plagiochila_punctata" "Plagiochila_asplenioides" "Plagiochila_porelloides"
[49] "Cephaloziella_divaricata" "Odontoschisma_denudatum" "Nowellia_curvifolia" "Lophozia_ventricosa"
[53] "Diplophyllum_albicans" "Diplophyllum_obtusifolium" "Anastrophyllum_minutum" "Jungermannia_exsertifolia"
[57] "Barbilophozia_barbata" "Barbilophozia_hatcheri" "Calypogeia_arguta" "Harpanthus_flotovianus"
[61] "Harpanthus_scutatus" "Jungermannia_obovata" "Nardia_scalaris" "Gymnomitrion_concinnatum"
[65] "Marsupella_emarginata" "Geocalyx_graveolens" "Jungermannia_pumila" "Jungermannia_atrovirens"
[69] "Saccogyna_viticulosa" "Calypogeia_fissa" "Calypogeia_integristipula" "Calypogeia_neesiana"
[73] "Polytrichum_commune" "Sphagnum_contortum" "Sphagnum_rubellum" "Sphagnum_squarrosum"
[77] "Sphagnum_fuscum" "Sphagnum_palustre" "Sphagnum_recurvum" "Sphagnum_fallax"
[81] "Sphagnum_cuspidatum" "Entosthodon_laevis" "Physcomitrella_patens" "Calymperes_palisotii"
[85] "Goniomitrium_acuminatum" "Dicranum_scoparium" "Philonotis_fontana" "Plagiothecium_curvifolium"
[89] "Sanionia_uncinata" "Brachythecium_salebrosum" "Brachythecium_acuminatum" "Brachythecium_rutabulum"
[93] "Hylocomium_splendens" "Pleurozium_schreberi" "Hypnum_cupressiforme" "Palustriella_falcata"
[97] "Anomodon_viticulosus" "Jaegerina_scariosa" "Hamatocaulis_vernicosus" "Phaeoceros_laevis"
[101] "Anthoceros_formosae" "Anthoceros_punctatus" "Huperzia_phyllantha" "Huperzia_drummondii"
[105] "Huperzia_australiana" "Huperzia_selago" "Huperzia_lucidula" "Lycopodiella_inundata"
[109] "Lycopodiella_cernua" "Lycopodium_casuarinoides" "Lycopodium_clavatum" "Lycopodium_deuterodensum"
[113] "Lycopodium_volubile" "Austrolycopodium_fastigiatum" "Diphasium_scariosum" "Lycopodium_annotinum"
[117] "Lycopodium_obscurum" "Diphasiastrum_complanatum" "Diphasiastrum_alpinum" "Isoetes_schweinfurthii"
[121] "Cephaloceraton_histrix" "Isoetes_echinospora" "Isoetes_lacustris" "Selaginella_selaginoides"
[125] "Selaginella_kraussiana" "Selaginella_remotifolia" "Selaginella_pygmaea" "Selaginella_uliginosa"
[129] "Selaginella_gracillima" "Selaginella_delicatissima" "Selaginella_helvetica" "Selaginella_frondosa"
[133] "Selaginella_longipinna" "Selaginella_moellendorffii" "Selaginella_tamariscina" "Selaginella_biformis"
[137] "Marattia_douglasii" "Angiopteris_wangii" "Angiopteris_hokouensis" "Angiopteris_caudatiformis"
[141] "Angiopteris_evecta" "Tmesipteris_truncata" "Psilotum_nudum" "Tmesipteris_tannensis"
[145] "Tmesipteris_obliqua" "Tmesipteris_elongata" "Botrychium_virginianum" "Botrychium_lanuginosum"
[149] "Botrychium_multifidum" "Botrychium_schaffneri" "Botrychium_matricariifolium" "Botrychium_lunaria"
[153] "Ophioglossum_nudicaule" "Ophioglossum_costatum" "Ophioglossum_pendulum" "Ophioglossum_petiolatum"
[157] "Ophioglossum_lusitanicum" "Ophioglossum_vulgatum" "Ophioglossum_reticulatum" "Equisetum_diffusum"
[161] "Equisetum_fluviatile" "Equisetum_telmateia" "Equisetum_palustre" "Equisetum_sylvaticum"
[165] "Equisetum_pratense" "Equisetum_arvense" "Equisetum_scirpoides" "Equisetum_variegatum"
[169] "Equisetum_ramosissimum" "Equisetum_hyemale" "Equisetum_laevigatum" "Equisetum_giganteum"
[173] "Osmunda_claytoniana" "Osmunda_vachellii" "Osmunda_japonica" "Osmunda_regalis"
[177] "Todea_barbara" "Leptopteris_fraseri" "Leptopteris_hymenophylloides" "Leptopteris_superba"
[181] "Dicranopteris_linearis" "Dicranopteris_pedata" "Dicranopteris_gigantea" "Diplopterygium_glaucoides"
[185] "Gleichenia_japonica" "Diplopterygium_glaucum" "Sticherus_laevigatus" "Sticherus_cunninghamii"
[189] "Gleichenia_dicarpa" "Gleichenia_microphylla" "Trichomanes_diversifrons" "Trichomanes_pinnatum"
[193] "Cephalomanes_javanicum" "Abrodictyum_rigidum" "Trichomanes_borbonicum" "Polyphlebium_venosum"
[197] "Didymoglossum_reptans" "Didymoglossum_erosum" "Crepidomanes_latealatum" "Crepidomanes_auriculatum"
[201] "Vandenboschia_collariata" "Hymenophyllum_pulcherrimum" "Hymenophyllum_poolii" "Hymenophyllum_flabellatum"
[205] "Hymenophyllum_scabrum" "Hymenophyllum_villosum" "Hymenophyllum_sanguinolentum" "Hymenophyllum_dilatatum"
[209] "Hymenophyllum_demissum" "Hymenophyllum_flexuosum" "Hymenophyllum_australe" "Hymenophyllum_minimum"
[213] "Hymenophyllum_darwinii" "Hymenophyllum_polyanthos" "Hymenophyllum_sibthorpioides" "Hymenophyllum_tunbrigense"
[217] "Hymenophyllum_peltatum" "Hymenophyllum_revolutum" "Hymenophyllum_cupressiforme" "Hymenophyllum_pumilum"
[221] "Schizaea_fistulosa" "Anemia_phyllitidis" "Schizaea_pectinata" "Schizaea_dichotoma"
[225] "Lygodium_japonicum" "Lygodium_circinatum" "Lygodium_kerstenii" "Cibotium_barometz"
[229] "Cibotium_chamissoi" "Cibotium_glaucum" "Calochlaena_dubia" "Dicksonia_fibrosia"
[233] "Balantium_antarcticum" "Dicksonia_baudouinii" "Dicksonia_squarrosa" "Cyathea_leichhardtiana"
[237] "Sphaeropteris_cooperi" "Cyathea_contaminans" "Sphaeropteris_brunoniana" "Cyathea_alata"
[241] "Cyathea_karsteniana" "Cyathea_senilis" "Cyathea_spinulosa" "Cyathea_australis"
[245] "Alsophila_cuspidata" "Cyathea_colensoi" "Salvinia_natans" "Salvinia_adnata"
[249] "Azolla_nilotica" "Azolla_pinnata" "Azolla_filiculoides" "Azolla_mexicana"
[253] "Azolla_microphylla" "Azolla_caroliniana" "Calamistrum_globuliferum" "Pilularia_novae"
[257] "Marsilea_mutica" "Marsilea_minuta" "Marsilea_quadrifolia" "Marsilea_drummondii"
[261] "Marsilea_capensis" "Marsilea_coromandelina" "Marsilea_villifolia" "Marsilea_vera"
[265] "Marsilea_aegyptiaca" "Marsilea_ephippiocarpa" "Marsilea_farinosa" "Marsilea_macrocarpa"
[269] "Saccoloma_elegans" "Saccoloma_inaequale" "Lindsaea_odorata" "Lindsaea_javanensis"
[273] "Lindsaea_ensifolia" "Cryptogramma_crispa" "Pityrogramma_calomelanos" "Anogramma_leptophylla"
[277] "Pteris_platyzomopsis" "Pteris_vittata" "Onychium_japonicum" "Pteris_tremula"
[281] "Pteris_semipinnata" "Pteris_setulosocostulata" "Pteris_ensiformis" "Pteris_excelsa"
[285] "Pteris_cretica" "Pteris_multifida" "Pteris_berteroana" "Pteris_microptera"
[289] "Cheilanthes_lanosa" "Pellaea_falcata" "Pellaea_rotundifolia" "Pellaea_boivinii"
[293] "Pellaea_dura" "Cheilanthes_argentea" "Pellaea_calomelanos" "Cheilanthes_multifida"
[297] "Cheilanthes_hirta" "Cheilanthes_eckloniana" "Cheilanthes_sieberi" "Cheilanthes_lasiophylla"
[301] "Cheilanthes_distans" "Anetium_citrifolium" "Vittaria_isoetifolia" "Vittaria_lineata"
[305] "Vittaria_stipitata" "Hecistopteris_pumila" "Haplopteris_volkensii" "Vittaria_ensiformis"
[309] "Vittaria_flexuosa" "Adiantum_capillus-veneris" "Adiantum_raddianum" "Adiantum_chilense"
[313] "Adiantum_hispidulum" "Adiantum_diaphanum" "Adiantum_pedatum" "Adiantum_obliquum"
[317] "Adiantum_latifolium" "Adiantum_flabellulatum" "Adiantum_bonatianum" "Adiantum_lunulatum"
[321] "Adiantum_caudatum" "Adiantum_malesianum" "Monachosorum_henryi" "Pteridium_aquilinum"
[325] "Pteridium_esculentum" "Histiopteris_incisa" "Hypolepis_millefolium" "Hypolepis_punctata"
[329] "Hypolepis_muelleri" "Dennstaedtia_punctilobula" "Dennstaedtia_scabra" "Microlepia_strigosa"
[333] "Microlepia_platyphylla" "Microlepia_speluncae" "Microlepia_hookeriana" "Microlepia_marginata"
[337] "Hymenasplenium_cardiophyllum" "Hymenasplenium_hondoense" "Asplenium_cheilosorum" "Hymenasplenium_excisum"
[341] "Asplenium_filipes" "Asplenium_unilaterale" "Asplenium_juglandifolium" "Ceterach_aurea"
[345] "Asplenium_ensiforme" "Asplenium_cuneatiforme" "Asplenium_caudatum" "Asplenium_polyodon"
[349] "Asplenium_sphenotomum" "Asplenium_affine" "Asplenium_wilfordii" "Asplenium_pseudowilfordii"
[353] "Asplenium_finlaysonianum" "Asplenium_yoshinagae" "Asplenium_nidus" "Asplenium_setoi"
[357] "Cyclosorus_parasiticus" "Asplenium_phyllitidis" "Asplenium_antrophyoides" "Asplenium_cymbifolium"
[361] "Asplenium_antiquum" "Asplenium_anisophyllum" "Asplenium_chathamense" "Asplenium_lamprophyllum"
[365] "Asplenium_shuttleworthianum" "Asplenium_dimorphum" "Asplenium_difforme" "Asplenium_hookerianum"
[369] "Asplenium_richardii" "Asplenium_viellardii" "Asplenium_oblongifolium" "Asplenium_obliquum"
[373] "Asplenium_flaccidum" "Asplenium_obtusatum" "Asplenium_simplicifrons" "Asplenium_prolongatum"
[377] "Asplenium_griffithianum" "Asplenium_tenerum" "Asplenium_rutifolium" "Asplenium_theciferum"
[381] "Asplenium_loxoscaphoides" "Asplenium_feei" "Asplenium_elliottii" "Asplenium_smedsii"
[385] "Asplenium_mannii" "Asplenium_christii" "Asplenium_sandersonii" "Asplenium_dregeanum"
[389] "Asplenium_gemmiferum" "Asplenium_lolegnamense" "Asplenium_parvifolium" "Asplenium_octoploideum"
[393] "Phyllitopsis_hybrida" "Ceterach_officinarum" "Asplenium_cyprium" "Asplenium_hemionitis"
[397] "Asplenium_septentrionale" "Asplenium_seelosii" "Asplenium_davallioides" "Asplenium_bulbiferum"
[401] "Asplenium_wrightii" "Asplenium_wrightioides" "Asplenium_variabile" "Asplenium_protensum"
[405] "Asplenium_aethiopicum" "Asplenium_praemorsum" "Asplenium_praegracile" "Asplenium_volkensii"
[409] "Asplenium_hemitomum" "Asplenium_friesiorum" "Asplenium_punjabense" "Asplenium_dalhousiae"
[413] "Asplenium_sagittatum" "Asplenium_scolopendrium" "Asplenium_hispanicum" "Asplenium_ruta"
[417] "Asplenium_onopteris" "Asplenium_cuneifolium" "Asplenium_montanum" "Asplenium_adiantum"
[421] "Asplenium_fissum" "Asplenium_aegaeum" "Asplenium_sulcatum" "Asplenium_abscissum"
[425] "Asplenium_hostmannii" "Asplenium_peruvianum" "Asplenium_pteropus" "Asplenium_cristatum"
[429] "Asplenium_myriophyllum" "Asplenium_lunulatum" "Asplenium_erectum" "Asplenium_viride"
[433] "Asplenium_hobdyi" "Asplenium_normale" "Asplenium_trichomanes" "Asplenium_tripteropus"
[437] "Asplenium_formosum" "Asplenium_heterochroum" "Asplenium_resiliens" "Asplenium_monanthes"
[441] "Asplenium_hallbergii" "Asplenium_dareoides" "Asplenium_flabellifolium" "Asplenium_pauperequitum"
[445] "Dicksonia_sellowiana" "Asplenium_haughtonii" "Asplenium_phillipsianum" "Asplenium_cordatum"
[449] "Asplenium_interjectum" "Asplenium_lushanense" "Asplenium_laciniatum" "Asplenium_varians"
[453] "Asplenium_tenuicaule" "Asplenium_pekinense" "Asplenium_sarelii" "Asplenium_platyneuron"
[457] "Asplenium_bourgaei" "Asplenium_jahandiezii" "Asplenium_ruprechtii" "Asplenium_radicans"
[461] "Asplenium_yunnanense" "Asplenium_foresiense" "Asplenium_incisum" "Asplenium_petrarchae"
[465] "Asplenium_majoricum" "Gymnocarpium_dryopteris" "Acystopteris_tenuisecta" "Cystopteris_fragilis"
[469] "Cystopteris_sudetica" "Macrothelypteris_torresiana" "Phegopteris_hexagonoptera" "Thelypteris_confluens"
[473] "Oreopteris_limbosperma" "Pronephrium_simplex" "Parathelypteris_beddomei" "Thelypteris_globulifera"
[477] "Thelypteris_noveboracensis" "Diplazium_wichurae" "Anisocampium_niponicum" "Athyrium_schimperi"
[481] "Athyrium_yokoscense" "Athyrium_alpestre" "Athyrium_filix" "Onoclea_sensibilis"
[485] "Matteuccia_struthiopteris" "Blechnum_orientale" "Sadleria_cyatheoides" "Blechnum_spicant"
[489] "Blechnum_occidentale" "Blechnum_patersonii" "Blechnum_capense" "Doodia_caudata"
[493] "Doodia_aspera" "Blechnum_medium" "Lomariopsis_spectabilis" "Nephrolepis_cordifolia"
[497] "Nephrolepis_hirsutula" "Humata_pyxidata" "Arthropteris_beckleri" "Arthropteris_palisotii"
[501] "Arthropteris_orientalis" "Arthropteris_monocarpa" "Tectaria_devexa" "Tectaria_yunnanensis"
[505] "Tectaria_decurrens" "Tectaria_incisa" "Tectaria_gaudichaudii" "Tectaria_zeylanica"
[509] "Tectaria_impressa" "Polypodium_australe" "Polypodium_pellucidum" "Polypodium_vulgare"
[513] "Polypodium_virginianum" "Campyloneurum_brevifolium" "Microgramma_lycopodioides" "Microgramma_squamulosa"
[517] "Grammitis_baldwinii" "Grammitis_diminuta" "Grammitis_billardierei" "Ctenopteris_heterophylla"
[521] "Grammitis_tenella" "Adenophorus_pinnatifidus" "Adenophorus_abietinus" "Adenophorus_tamariscinus"
[525] "Platycerium_alcicorne" "Platycerium_stemaria" "Pyrrosia_adnascens" "Pyrrosia_eleagnifolia"
[529] "Pyrrosia_serpens" "Pleopeltis_polypodioides" "Serpocaulon_triseriale" "Bolbitis_heteroclita"
[533] "Colysis_hemitoma" "Colysis_pentaphylla" "Microsorum_punctatum" "Phymatosorus_scolopendria"
[537] "Neocheiropteris_palmatopedata" "Neocheiropteris_ovata" "Lepisorus_scolopendrius" "Lepisorus_macrosphaerus"
[541] "Tricholepidium_maculosum" "Lepisorus_thunbergianus" "Lepisorus_contortus" "Rumohra_adiantiformis"
[545] "Lastreopsis_decomposita" "Lastreopsis_hispida" "Elaphoglossum_peltatum" "Elaphoglossum_hybridum"
[549] "Elaphoglossum_aubertii" "Elaphoglossum_piloselloides" "Elaphoglossum_acrostichoides" "Elaphoglossum_petiolatum"
[553] "Elaphoglossum_marojejyense" "Elaphoglossum_latifolium" "Elaphoglossum_herminieri" "Ctenitis_rhodolepis"
[557] "Cyrtomium_fortunei" "Polystichum_tripteron" "Polystichum_dielsii" "Polystichum_luctuosum"
[561] "Polystichum_tsus" "Polystichum_aculeatum" "Polystichum_setiferum" "Polystichum_pycnopterum"
[565] "Polystichum_eximium" "Polystichum_neozelandicum" "Polystichum_yunnanense" "Polystichum_chilense"
[569] "Polystichum_munitum" "Polystichum_acrostichoides" "Polystichum_mohrioides" "Polystichum_lonchitis"
[573] "Polystichum_montevidense" "Polybotrya_caudata" "Arachniodes_rhomboidea" "Arachniodes_festina"
[577] "Arachniodes_simplicior" "Arachniodes_globisora" "Arachniodes_sporadosora" "Acrophorus_stipellatus"
[581] "Dryopteris_stenolepis" "Dryopteris_wallichiana" "Dryopteris_lepidopoda" "Dryopteris_oreades"
[585] "Dryopteris_filix" "Dryopteris_marginalis" "Dryopteris_chrysocoma" "Dryopteris_pseudovaria"
[589] "Dryopteris_sublacera" "Dryopteris_cochleata" "Dryopteris_lacera" "Dryopteris_aemula"
[593] "Nothoperanema_squamisetum" "Nothoperanema_hendersonii" "Dryopsis_mariformis" "Dryopteris_setosa"
[597] "Dryopteris_fuscipes" "Dryopteris_erythrosora" "Dryopteris_sparsa" "Dryopteris_juxtaposita"
[601] "Dryopteris_goldiana" "Dryopteris_intermedia" "Dryopteris_dilatata" "Dryopteris_remota"
[605] "Dryopteris_carthusiana" "Dryopteris_expansa" "Dryopteris_cristata" "Ginkgo_biloba"
[609] "Cycas_micholitzii" "Cycas_multipinnata" "Cycas_pectinata" "Cycas_silvestris"
[613] "Cycas_condaoensis" "Cycas_simplicipinna" "Cycas_tanqingii" "Cycas_szechuanensis"
[617] "Cycas_wadei" "Cycas_taiwaniana" "Cycas_taitungensis" "Cycas_revoluta"
[621] "Cycas_furfuracea" "Cycas_hoabinhensis" "Cycas_media" "Cycas_rumphii"
[625] "Cycas_lindstromii" "Cycas_circinalis" "Cycas_seemannii" "Cycas_thouarsii"
[629] "Cycas_zeylanica" "Cycas_edentata" "Cycas_clivicola" "Cycas_siamensis"
[633] "Cycas_pranburiensis" "Cycas_tansachana" "Cycas_chamaoensis" "Cycas_nongnoochiae"
[637] "Cycas_segmentifida" "Cycas_changjiangensis" "Encephalartos_altensteinii" "Dioon_spinulosum"
[641] "Dioon_mejiae" "Dioon_rzedowskii" "Dioon_edule" "Dioon_tomasellii"
[645] "Dioon_purpusii" "Dioon_holmgrenii" "Dioon_merolae" "Dioon_califanoi"
[649] "Dioon_caputoi" "Stangeria_eriopus" "Microcycas_calocoma" "Zamia_poeppigiana"
[653] "Zamia_pumila" "Zamia_vazquezii" "Zamia_restrepoi" "Zamia_furfuracea"
[657] "Ceratozamia_miqueliana" "Ceratozamia_robusta" "Ceratozamia_microstrobila" "Ceratozamia_norstogii"
[661] "Ceratozamia_kuesteriana" "Ceratozamia_mexicana" "Ceratozamia_sabatoi" "Ceratozamia_euryphyllidia"
[665] "Ceratozamia_morettii" "Ceratozamia_alvarezii" "Ceratozamia_hildae" "Bowenia_spectabilis"
[669] "Bowenia_serrulata" "Macrozamia_fraseri" "Macrozamia_miquelii" "Macrozamia_platyrhachis"
[673] "Macrozamia_fawcettii" "Macrozamia_reducta" "Macrozamia_macdonnellii" "Macrozamia_riedlei"
[677] "Macrozamia_glaucophylla" "Macrozamia_polymorpha" "Macrozamia_pauli" "Macrozamia_communis"
[681] "Macrozamia_lucida" "Macrozamia_moorei" "Macrozamia_dyeri" "Lepidozamia_hopei"
[685] "Lepidozamia_peroffskyana" "Encephalartos_tegulaneus" "Encephalartos_septentrionalis" "Encephalartos_bubalinus"
[689] "Encephalartos_kisambo" "Encephalartos_macrostrobilus" "Encephalartos_whitelockii" "Encephalartos_gratus"
[693] "Encephalartos_hildebrandtii" "Encephalartos_dyerianus" "Encephalartos_latifrons" "Encephalartos_transvenosus"
[697] "Encephalartos_munchii" "Encephalartos_pterogonus" "Encephalartos_humilis" "Encephalartos_eugene"
[701] "Encephalartos_lanatus" "Encephalartos_ghellinckii" "Encephalartos_paucidentatus" "Encephalartos_cycadifolius"
[705] "Encephalartos_laevifolius" "Encephalartos_lebomboensis" "Encephalartos_arenarius" "Encephalartos_horridus"
[709] "Encephalartos_cupidus" "Encephalartos_trispinosus" "Encephalartos_inopinus" "Encephalartos_princeps"
[713] "Encephalartos_lehmannii" "Encephalartos_barteri" "Encephalartos_manikensis" "Encephalartos_longifolius"
[717] "Encephalartos_friderici" "Encephalartos_villosus" "Encephalartos_caffer" "Encephalartos_woodii"
[721] "Encephalartos_natalensis" "Encephalartos_ferox" "Encephalartos_ngoyanus" "Encephalartos_senticosus"
[725] "Encephalartos_heenanii" "Encephalartos_umbeluziensis" "Welwitschia_mirabilis" "Gnetum_leyboldii"
[729] "Gnetum_nodiflorum" "Gnetum_urens" "Gnetum_costatum" "Gnetum_gnemon"
[733] "Gnetum_africanum" "Gnetum_gnemonoides" "Gnetum_parvifolium" "Gnetum_montanum"
[737] "Gnetum_cuspidatum" "Gnetum_macrostachyum" "Gnetum_latifolium" "Gnetum_ula"
[741] "Gnetum_neglectum" "Ephedra_tweediana" "Ephedra_frustillata" "Ephedra_chilensis"
[745] "Ephedra_nevadensis" "Ephedra_torreyana" "Ephedra_californica" "Ephedra_aspera"
[749] "Ephedra_trifurca" "Ephedra_viridis" "Ephedra_gerardiana" "Ephedra_equisetina"
[753] "Ephedra_przewalskii" "Ephedra_minuta" "Ephedra_pachyclada" "Ephedra_sinica"
[757] "Ephedra_intermedia" "Ephedra_distachya" "Ephedra_regeliana" "Ephedra_foeminea"
[761] "Ephedra_fragilis" "Ephedra_foliata" "Ephedra_alata" "Ephedra_aphylla"
[765] "Cedrus_libani" "Cedrus_atlantica" "Cedrus_deodara" "Keteleeria_davidiana"
[769] "Pseudolarix_amabilis" "Nothotsuga_longibracteata" "Tsuga_mertensiana" "Tsuga_heterophylla"
[773] "Tsuga_dumosa" "Tsuga_diversifolia" "Tsuga_caroliniana" "Tsuga_sieboldii"
[777] "Tsuga_forrestii" "Tsuga_chinensis" "Keteleeria_evelyniana" "Abies_bracteata"
[781] "Abies_firma" "Abies_fabri" "Abies_holophylla" "Abies_fargesii"
[785] "Abies_fraseri" "Abies_koreana" "Abies_grandis" "Abies_recurvata"
[789] "Abies_nephrolepis" "Abies_kawakamii" "Abies_homolepis" "Abies_procera"
[793] "Abies_amabilis" "Abies_magnifica" "Abies_alba" "Abies_nebrodensis"
[797] "Abies_nordmanniana" "Abies_pinsapo" "Abies_numidica" "Abies_veitchii"
[801] "Abies_cilicica" "Abies_cephalonica" "Pseudotsuga_sinensis" "Pseudotsuga_macrocarpa"
[805] "Larix_gmelinii" "Larix_decidua" "Larix_occidentalis" "Larix_laricina"
[809] "Larix_sibirica" "Larix_kaempferi" "Larix_griffithii" "Larix_mastersiana"
[813] "Larix_potaninii" "Cathaya_argyrophylla" "Picea_sitchensis" "Picea_breweriana"
[817] "Picea_glauca" "Picea_engelmannii" "Picea_pungens" "Picea_chihuahuana"
[821] "Picea_schrenkiana" "Picea_spinulosa" "Picea_likiangensis" "Picea_orientalis"
[825] "Picea_alcoquiana" "Picea_morrisonicola" "Picea_brachytyla" "Picea_purpurea"
[829] "Picea_wilsonii" "Picea_glehnii" "Picea_jezoensis" "Picea_rubens"
[833] "Picea_omorika" "Picea_mariana" "Picea_abies" "Picea_asperata"
[837] "Picea_koyamae" "Picea_obovata" "Picea_meyeri" "Picea_koraiensis"
[841] "Picea_crassifolia" "Picea_torano" "Picea_retroflexa" "Pinus_nelsonii"
[845] "Pinus_balfouriana" "Pinus_aristata" "Pinus_monophylla" "Pinus_quadrifolia"
[849] "Pinus_cembroides" "Pinus_edulis" "Pinus_maximartinezii" "Pinus_parviflora"
[853] "Pinus_fenzeliana" "Pinus_armandii" "Pinus_albicaulis" "Pinus_lambertiana"
[857] "Abies_sibirica" "Pinus_pumila" "Pinus_bhutanica" "Pinus_monticola"
[861] "Pinus_peuce" "Pinus_krempfii" "Pinus_gerardiana" "Pinus_bungeana"
[865] "Pinus_koraiensis" "Pinus_strobus" "Pinus_flexilis" "Pinus_strobiformis"
[869] "Pinus_banksiana" "Pinus_heldreichii" "Pinus_canariensis" "Pinus_brutia"
[873] "Pinus_pinaster" "Pinus_pinea" "Pinus_tropicalis" "Pinus_massoniana"
[877] "Pinus_merkusii" "Pinus_densiflora" "Pinus_taiwanensis" "Pinus_thunbergii"
[881] "Pinus_kesiya" "Pinus_densata" "Pinus_yunnanensis" "Pinus_tabuliformis"
[885] "Pinus_hwangshanensis" "Pinus_luchuensis" "Pinus_clausa" "Pinus_virginiana"
[889] "Pinus_arizonica" "Pinus_douglasiana" "Pinus_coulteri" "Pinus_engelmannii"
[893] "Pinus_jeffreyi" "Pinus_devoniana" "Pinus_hartwegii" "Pinus_montezumae"
[897] "Pinus_sabiniana" "Pinus_ayacahuite" "Pinus_ponderosa" "Pinus_torreyana"
[901] "Pinus_taeda" "Pinus_pungens" "Pinus_serotina" "Pinus_muricata"
[905] "Pinus_attenuata" "Pinus_glabra" "Pinus_teocote" "Pinus_leiophylla"
[909] "Pinus_greggii" "Pinus_lumholtzii" "Pinus_lawsonii" "Pinus_herrerae"
[913] "Pinus_pringlei" "Pinus_oocarpa" "Pinus_cubensis" "Pinus_palustris"
[917] "Pinus_caribaea" "Pinus_elliottii" "Pinus_echinata" "Sciadopitys_verticillata"
[921] "Cephalotaxus_harringtonia" "Cephalotaxus_fortunei" "Cephalotaxus_harringtonii" "Amentotaxus_yunnanensis"
[925] "Amentotaxus_argotaenia" "Torreya_grandis" "Torreya_californica" "Torreya_taxifolia"
[929] "Torreya_nucifera" "Austrotaxus_spicata" "Pseudotaxus_chienii" "Taxus_floridana"
[933] "Taxus_globosa" "Taxus_brevifolia" "Taxus_x" "Taxus_wallichiana"
[937] "Taxus_canadensis" "Taxus_cuspidata" "Cunninghamia_lanceolata" "Taiwania_cryptomerioides"
[941] "Metasequoia_glyptostroboides" "Sequoiadendron_giganteum" "Sequoia_sempervirens" "Athrotaxis_laxifolia"
[945] "Athrotaxis_selaginoides" "Athrotaxis_cupressoides" "Cryptomeria_japonica" "Glyptostrobus_pensilis"
[949] "Taxodium_huegelii" "Taxodium_distichum" "Papuacedrus_papuana" "Austrocedrus_chilensis"
[953] "Libocedrus_plumosa" "Pilgerodendron_uviferum" "Libocedrus_yateensis" "Libocedrus_bidwillii"
[957] "Diselma_archeri" "Fitzroya_cupressoides" "Widdringtonia_wallichii" "Widdringtonia_nodiflora"
[961] "Widdringtonia_schwarzii" "Callitris_preissii" "Actinostrobus_pyramidalis" "Actinostrobus_acuminatus"
[965] "Callitris_endlicheri" "Callitris_rhomboidea" "Thujopsis_dolabrata" "Thuja_koraiensis"
[969] "Thuja_standishii" "Thuja_plicata" "Chamaecyparis_obtusa" "Chamaecyparis_lawsoniana"
[973] "Fokienia_hodginsii" "Chamaecyparis_thyoides" "Chamaecyparis_formosensis" "Chamaecyparis_pisifera"
[977] "Tetraclinis_articulata" "Platycladus_orientalis" "Microbiota_decussata" "Calocedrus_decurrens"
[981] "Calocedrus_macrolepis" "Calocedrus_formosana" "Cupressus_funebris" "Cupressus_sempervirens"
[985] "Cupressus_chengiana" "Cupressus_dupreziana" "Cupressus_gigantea" "Cupressus_duclouxiana"
[989] "Cupressus_cashmeriana" "Cupressus_torulosa" "Cupressus_nootkatensis" "Cupressus_bakeri"
[993] "Cupressus_arizonica" "Cupressus_lusitanica" "Cupressus_guadalupensis" "Cupressus_macnabiana"
[997] "Cupressus_macrocarpa" "Cupressus_sargentii" "Cupressus_goveniana" "Juniperus_cedrus"
[ reached getOption("max.print") -- omitted 29499 entries ]Bayesian update functions:
## Test 3 different functions.
## 1. Step function with 3 parameters:
.updateStepFxTheta <- function(pars, cache, d, ct, move, prior=NULL){
pars.new <- bayou:::.slidingWindowProposal(cache, pars, d, move, ct)$pars
hr <- 0
pars.new$theta <- ifelse(midbins>pars.new$beta_center, pars.new$beta_right, pars.new$beta_left)
return(list(pars=pars.new, hr=hr))
}
## 2. Sigmoid function with 5 parameters
.updateSigmoidFxTheta <- function(pars, cache, d, ct, move, prior=NULL){
pars.new <- bayou:::.slidingWindowProposal(cache, pars, d, move, ct)$pars
hr <- 0
pars.new$theta <- pars.new$beta_left + pars.new$beta_right/(1 + exp(pars.new$beta_slope * (midbins - pars.new$beta_center)))
return(list(pars=pars.new, hr=hr))
}
## 3. Linear function with 2 parameters
.updateLinearFxTheta <- function(pars, cache, d, ct, move, prior=NULL){
pars.new <- bayou:::.slidingWindowProposal(cache, pars, d, move, ct)$pars
hr <- 0
pars.new$theta <- pars.new$beta_intercept + pars.new$beta_slope * midbins
return(list(pars=pars.new, hr=hr))
}Bayesian update functions for dealing with simmap reconstructions
## Other needed functions
.newMap <- function(pars, cache, d, ct, prior=NULL, move=NULL){
pars.new <- pars
stree <- reorderSimmap(make.simmap(cache$phy, setNames(cache$pred[[1]], cache$phy$tip.label), Q=Q, message=FALSE), "postorder")
shifts <- sapply(stree$maps, function(x) names(x)[-1])
pars.new$sb <- unlist(sapply(1:length(shifts), function(x) rep(x, length(shifts[[x]]))))
pars.new$t2 <- as.numeric(unlist(sapply(stree$maps, function(x) names(x)[-1])))
pars.new$loc <- unlist(sapply(stree$maps, function(x) cumsum(x)[-1]))
pars.new$k <- length(pars.new$sb)
hr <- 0
return(list(pars=pars.new, hr=Inf))
}
.newMapPresample <- function(pars, cache, d, ct, prior=NULL, move=NULL){
pars.new <- pars
index <- sample(1:length(strees.bayou), 1, replace=FALSE)
pars.new$sb <- strees.bayou[[index]]$sb #unlist(sapply(1:length(shifts), function(x) rep(x, length(shifts[[x]]))))
pars.new$t2 <- strees.bayou[[index]]$t2 #as.numeric(unlist(sapply(stree$maps, function(x) names(x)[-1])))
pars.new$loc <- unname(strees.bayou[[index]]$loc) #unlist(sapply(stree$maps, function(x) cumsum(x)[-1]))
pars.new$k <- length(pars.new$sb)
hr <- 0
return(list(pars=pars.new, hr=Inf))
}
## Empty functions to replace the deterministically specified theta values
dnull <- function(x, log=TRUE){
if(log) 0 else 1
}
rnull <- function(x){
return(x)
}Place the predictor into bins and transform the tree to unit height.
## Set up the bins:
ncat <- 10
pred <- "tmin.01"
trait <- "lnVs"
bins <- seq(min(td[[pred]])-0.1, max(td[[pred]])+0.1, length.out=ncat)
midbins <- (bins[2:length(bins)] + bins[1:(length(bins)-1)])/2
td <- mutate(td, binpred = as.numeric(cut(td[[pred]], breaks=bins, include.lowest=TRUE)))
hist(td[['binpred']])
td$phy$edge.length <- td$phy$edge.length/max(branching.times(td$phy))
tree <- td$phy
dat <- td$datFit a meristic model to the binned predictor data and construct a set of stochastic maps.
## Use diversitree to fit a meristic model:
mkn.lik <- make.mkn.meristic(td$phy, td[['binpred']], k = ncat)
mkn.lik <- constrain(mkn.lik, q.up~q.down)
p <- c(0.1)
fit.mkn <- find.mle(mkn.lik, p)
Q <- matrix(0, ncol=ncat-1, nrow=ncat-1)
for(i in 1:(ncat-2)){
Q[i, i+1] <- fit.mkn$par[1]
Q[i+1, i] <- fit.mkn$par[1]
}
diag(Q) <- apply(Q, 1, sum)*-1
rownames(Q) <- colnames(Q) <- 1:ncol(Q)
simmap2bayou <- function(stree){
shifts <- sapply(stree$maps, function(x) names(x)[-1])
sb <- unlist(sapply(1:length(shifts), function(x) rep(x, length(shifts[[x]]))))
t2 <- as.numeric(unlist(sapply(stree$maps, function(x) names(x)[-1])))
loc <- unlist(sapply(stree$maps, function(x) cumsum(x)[-1]))
return(list(shifts=shifts, sb=sb, t2=t2, loc=loc))
}
#fdfit <- fitDiscrete(td$phy, td[['binpred']], model="meristic")
#Qg <- geiger:::.Qmatrix.from.gfit(fdfit)
td <- reorder(td, "postorder")
stree <- reorderSimmap(make.simmap(td$phy, td[['binpred']], Q=Q), "postorder")make.simmap is sampling character histories conditioned on the transition matrix
Q =
1 2 3 4 5 6 7 8
1 -9.886328 9.886328 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
2 9.886328 -19.772656 9.886328 0.000000 0.000000 0.000000 0.000000 0.000000
3 0.000000 9.886328 -19.772656 9.886328 0.000000 0.000000 0.000000 0.000000
4 0.000000 0.000000 9.886328 -19.772656 9.886328 0.000000 0.000000 0.000000
5 0.000000 0.000000 0.000000 9.886328 -19.772656 9.886328 0.000000 0.000000
6 0.000000 0.000000 0.000000 0.000000 9.886328 -19.772656 9.886328 0.000000
7 0.000000 0.000000 0.000000 0.000000 0.000000 9.886328 -19.772656 9.886328
8 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 9.886328 -19.772656
9 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 9.886328
9
1 0.000000
2 0.000000
3 0.000000
4 0.000000
5 0.000000
6 0.000000
7 0.000000
8 9.886328
9 -9.886328
(specified by the user);
and (mean) root node prior probabilities
pi =
1 2 3 4 5 6 7 8
0.1111111 0.1111111 0.1111111 0.1111111 0.1111111 0.1111111 0.1111111 0.1111111
9
0.1111111 Some probabilities (slightly?) < 0. Setting p < 0 to zero.Done.strees <- make.simmap(td$phy, td[['binpred']], Q=Q, nsim=100)make.simmap is sampling character histories conditioned on the transition matrix
Q =
1 2 3 4 5 6 7 8
1 -9.886328 9.886328 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
2 9.886328 -19.772656 9.886328 0.000000 0.000000 0.000000 0.000000 0.000000
3 0.000000 9.886328 -19.772656 9.886328 0.000000 0.000000 0.000000 0.000000
4 0.000000 0.000000 9.886328 -19.772656 9.886328 0.000000 0.000000 0.000000
5 0.000000 0.000000 0.000000 9.886328 -19.772656 9.886328 0.000000 0.000000
6 0.000000 0.000000 0.000000 0.000000 9.886328 -19.772656 9.886328 0.000000
7 0.000000 0.000000 0.000000 0.000000 0.000000 9.886328 -19.772656 9.886328
8 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 9.886328 -19.772656
9 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 9.886328
9
1 0.000000
2 0.000000
3 0.000000
4 0.000000
5 0.000000
6 0.000000
7 0.000000
8 9.886328
9 -9.886328
(specified by the user);
and (mean) root node prior probabilities
pi =
1 2 3 4 5 6 7 8
0.1111111 0.1111111 0.1111111 0.1111111 0.1111111 0.1111111 0.1111111 0.1111111
9
0.1111111 Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Done.for(i in 1:length(strees)) strees[[i]] <- reorderSimmap(strees[[i]], "postorder")
strees.bayou <- lapply(strees, simmap2bayou)
plotSimmap(stree, col=setNames(cm.colors(ncat), 1:ncat))
model.stepTheta$lik.fn(pp, cache, cache$dat)$loglik [,1]
[1,] -439.3852shifts <- sapply(stree$maps, function(x) names(x)[-1])
sb <- unlist(sapply(1:length(shifts), function(x) rep(x, length(shifts[[x]]))))
t2 <- as.numeric(unlist(sapply(stree$maps, function(x) names(x)[-1])))
loc <- unname(unlist(sapply(stree$maps, function(x) cumsum(x)[-1])))
tmp <- lm(td[[trait]]~td[[pred]]+I(td[[pred]]^2)+I(td[[pred]]^3))strees.bayou <- readRDS("../output/OUwie/strees.bayou.rds")## Starting parameters for each function type:
## Step function
step.starts <- c(0, -8, -4)
startpar.step <- list(alpha=1, sig2=0.5,
beta_left=step.starts[2],
beta_right=step.starts[3],
beta_center=step.starts[1],
k=length(sb), ntheta=ncat-1,
theta=ifelse(midbins > step.starts[1], step.starts[3], step.starts[2]),
sb=sb, loc=loc, t2=t2)
plot(midbins, startpar.step$theta)
sigmoid.starts <- list(beta_left = -2, beta_right = -6, beta_center = 0, beta_slope = 0.02)
startpar.sigmoid <- list(alpha=1, sig2=0.5,
beta_left=sigmoid.starts$beta_left,
beta_right=sigmoid.starts$beta_right,
beta_center=sigmoid.starts$beta_center,
beta_slope = sigmoid.starts$beta_slope,
k=length(sb), ntheta=ncat-1,
theta= sigmoid.starts$beta_left + sigmoid.starts$beta_right/(1 + exp(sigmoid.starts$beta_slope * (midbins - sigmoid.starts$beta_center))),
sb=sb, loc=loc, t2=t2)
plot(midbins, startpar.sigmoid$theta)
linear.starts <- list(beta_intercept = -5, beta_slope = 0.01)
startpar.linear <- list(alpha=1, sig2=0.5,
beta_intercept=linear.starts$beta_intercept,
beta_slope=linear.starts$beta_slope,
k=length(sb), ntheta=ncat-1,
theta= linear.starts$beta_intercept + linear.starts$beta_slope*midbins,
sb=sb, loc=loc, t2=t2)
plot(midbins, startpar.linear$theta)
## Set priors
prior.step <- make.prior(td$phy, plot.prior = FALSE,
dists=list(dalpha="dlnorm", dsig2="dhalfcauchy",
dbeta_left="dunif",
dbeta_right="dunif",
dbeta_center="dunif",
dsb="fixed", dk="fixed",
dtheta="dnull", dloc="fixed"),
param=list(dalpha=list(meanlog=1, sdlog=1), dsig2=list(scale=0.1),
dbeta_left=list(min=-12, max=0),
dbeta_right=list(min=-12, max=0),
dbeta_center=list(min=-100, max=100),
dk="fixed",
dsb="fixed",
dtheta=list(), dloc="fixed"),
fixed=list(k=length(sb), ntheta=ncat-1, sb=sb, loc=loc, t2=t2)
)
prior.sigmoid <- make.prior(td$phy, plot.prior = FALSE,
dists=list(dalpha="dlnorm", dsig2="dhalfcauchy",
dbeta_left="dunif",
dbeta_right="dunif",
dbeta_center="dunif",
dbeta_slope="dlnorm",
dsb="fixed", dk="fixed",
dtheta="dnull", dloc="fixed"),
param=list(dalpha=list(meanlog=1, sdlog=1), dsig2=list(scale=0.1),
dbeta_left=list(min=-12, max=0),
dbeta_right=list(min=-12, max=0),
dbeta_center=list(min=-100, max=100),
dbeta_slope=list(meanlog=-3, sdlog=1),
dk="fixed",
dsb="fixed",
dtheta=list(), dloc="fixed"),
fixed=list(k=length(sb), ntheta=ncat-1, sb=sb, loc=loc, t2=t2)
)
prior.linear <- make.prior(td$phy, plot.prior = FALSE,
dists=list(dalpha="dlnorm", dsig2="dhalfcauchy",
dbeta_intercept="dnorm",
dbeta_slope="dnorm",
dsb="fixed", dk="fixed",
dtheta="dnull", dloc="fixed"),
param=list(dalpha=list(meanlog=1, sdlog=1), dsig2=list(scale=0.1),
dbeta_intercept=list(mean=-6, sd=1.5),
dbeta_slope=list(mean=0, sd=0.1),
dk="fixed",
dsb="fixed",
dtheta=list(), dloc="fixed"),
fixed=list(k=length(sb), ntheta=ncat-1, sb=sb, loc=loc, t2=t2)
)model.stepTheta <- list(moves = list(alpha=".multiplierProposal",
sig2=".multiplierProposal",
beta_left=".updateStepFxTheta",
beta_right=".updateStepFxTheta",
beta_center=".updateStepFxTheta",
theta=".newMapPresample"),
control.weights = list(alpha=10, sig2=10,
beta_left=10,
beta_right=10,
beta_center=10,
theta=1,
k=0),
D = list(alpha=1, sig2=1,
beta_left=0.2,
beta_right=0.2,
beta_center=20,
theta = 1),
parorder = c("alpha", "sig2", "beta_left", "beta_right", "beta_center", "theta", "k", "ntheta"),
rjpars = c(),
shiftpars = c("sb", "loc", "t2"),
lik.fn = bayou.lik)
model.stepTheta$monitor.fn <- function(i, lik, pr, pars, accept, accept.type, j){
names <- c("gen", "lnL", "prior", "alpha","sig2", "beta_left", "beta_right", "beta_center", "k") #The parameters we want to print
string <- "%-8i%-8.2f%-8.2f%-8.2f%-8.2f%-8.2f%-8.2f%-8.2f%-8i" #The types of each parameter
acceptratios <- tapply(accept, accept.type, mean)
names <- c(names, names(acceptratios))
if(j==0){
cat(sprintf("%-7.7s", names), "\n", sep=" ")
}
cat(sprintf(string, i, lik, pr, pars$alpha, pars$sig2[1], pars$beta_left, pars$beta_right, pars$beta_center, pars$k), sprintf("%-8.2f", acceptratios),"\n", sep="")
}model.sigmoidTheta <- list(moves = list(alpha=".multiplierProposal",
sig2=".multiplierProposal",
beta_left=".updateSigmoidFxTheta",
beta_right=".updateSigmoidFxTheta",
beta_center=".updateSigmoidFxTheta",
beta_slope=".updateSigmoidFxTheta",
theta=".newMapPresample"),
control.weights = list(alpha=10, sig2=10,
beta_left=10,
beta_right=10,
beta_center=10,
beta_slope=10,
theta=1,
k=0),
D = list(alpha=1, sig2=1,
beta_left=0.2,
beta_right=0.2,
beta_center=20,
beta_slope=0.005,
theta = 1),
parorder = c("alpha", "sig2", "beta_left", "beta_right", "beta_center", "beta_slope", "theta", "k", "ntheta"),
rjpars = c(),
shiftpars = c("sb", "loc", "t2"),
lik.fn = bayou.lik)
model.sigmoidTheta$monitor.fn <- function(i, lik, pr, pars, accept, accept.type, j){
names <- c("gen", "lnL", "prior", "alpha","sig2", "beta_left", "beta_right", "beta_center", "beta_slope", "k") #The parameters we want to print
string <- "%-8i%-8.2f%-8.2f%-8.2f%-8.2f%-8.2f%-8.2f%-8.2f%-8.2f%-8i" #The types of each parameter
acceptratios <- tapply(accept, accept.type, mean)
names <- c(names, names(acceptratios))
if(j==0){
cat(sprintf("%-7.7s", names), "\n", sep=" ")
}
cat(sprintf(string, i, lik, pr, pars$alpha, pars$sig2[1], pars$beta_left, pars$beta_right, pars$beta_center, pars$beta_slope, pars$k), sprintf("%-8.2f", acceptratios),"\n", sep="")
}model.linearTheta <- list(moves = list(alpha=".multiplierProposal",
sig2=".multiplierProposal",
beta_intercept=".updateLinearFxTheta",
beta_slope=".updateLinearFxTheta",
theta=".newMapPresample"),
control.weights = list(alpha=10, sig2=10,
beta_intercept=10,
beta_slope=10,
theta=1,
k=0),
D = list(alpha=1, sig2=1,
beta_intercept=0.5,
beta_slope=0.005,
theta = 1),
parorder = c("alpha", "sig2", "beta_intercept", "beta_slope", "theta", "k", "ntheta"),
rjpars = c(),
shiftpars = c("sb", "loc", "t2"),
lik.fn = bayou.lik)
model.linearTheta$monitor.fn <- function(i, lik, pr, pars, accept, accept.type, j){
names <- c("gen", "lnL", "prior", "alpha","sig2", "beta_intercept", "beta_slope", "k") #The parameters we want to print
string <- "%-8i%-8.2f%-8.2f%-8.2f%-8.2f%-8.2f%-8.2f%-8i" #The types of each parameter
acceptratios <- tapply(accept, accept.type, mean)
names <- c(names, names(acceptratios))
if(j==0){
cat(sprintf("%-7.7s", names), "\n", sep=" ")
}
cat(sprintf(string, i, lik, pr, pars$alpha, pars$sig2[1], pars$beta_intercept, pars$beta_slope, pars$k), sprintf("%-8.2f", acceptratios),"\n", sep="")
}## Just a bunch of tests
cache <- bayou:::.prepare.ou.univariate(td$phy, td[['lnVs']], SE=0, pred=td['binpred'])
prior.step(startpar.step)[1] -13.09416model.stepTheta$lik.fn(startpar.step, cache, cache$dat)$loglik [,1]
[1,] -24543.32ct <- bayou:::.buildControl(startpar.step, prior.step, move.weights=model.stepTheta$control.weights)
pars.new <- .newMapPresample(startpar.step, cache, d=model.stepTheta$D$theta, ct = ct)$pars
prior.step(pars.new)[1] -13.09416model.stepTheta$lik.fn(pars.new, cache, cache$dat)$loglik [,1]
[1,] -24558.23pp <- pars.new
prior.step(pp)[1] -13.09416model.stepTheta$lik.fn(pp, cache, cache$dat)$loglik [,1]
[1,] -24558.23new.pp <- .newMapPresample(pp, cache, 1, ct)$pars
model.stepTheta$lik.fn(new.pp, cache, cache$dat)$loglik [,1]
[1,] -24518.64prior.linear(startpar.linear)[1] -2.994012new.pp <- .newMapPresample(startpar.linear, cache, 1, ct)$pars
model.linearTheta$lik.fn(new.pp, cache, cache$dat)$loglik [,1]
[1,] -24590.84mymcmc.step <- bayou.makeMCMC(td$phy, td[[trait]], pred=td['binpred'], SE=0.02, model=model.stepTheta, prior=prior.step, startpar=startpar.step, new.dir=TRUE, outname="stepTheta_r2", plot.freq=NULL, ticker.freq=2000, samp = 100)
mymcmc.sigmoid <- bayou.makeMCMC(td$phy, td[[trait]], pred=td['binpred'], SE=0.02, model=model.sigmoidTheta, prior=prior.sigmoid, startpar=startpar.sigmoid, new.dir=TRUE, outname="sigmoidTheta_r2", plot.freq=NULL, ticker.freq=2000, samp = 100)
mymcmc.linear <- bayou.makeMCMC(td$phy, td[[trait]], pred=td['binpred'], SE=0.02, model=model.linearTheta, prior=prior.linear, startpar=startpar.linear, new.dir=TRUE, outname="linearTheta_r2", plot.freq=NULL, ticker.freq=2000, samp = 100)mymcmc.step$run(100000)gen lnL prior alpha sig2 beta_le beta_ri beta_ce k .alpha .beta_c .beta_l .beta_r .sig2 .theta
2000 -590.84 -38.20 123.17 440.79 -6.55 -4.65 13.25 896 0.31 0.93 0.78 0.79 0.25 1.00
4000 -592.19 -37.47 107.69 420.95 -6.31 -4.69 27.22 892 0.29 0.93 0.79 0.80 0.23 1.00
6000 -583.48 -38.62 132.64 454.67 -6.53 -4.72 23.81 877 0.27 0.93 0.80 0.81 0.24 1.00
8000 -589.63 -39.31 147.54 493.52 -6.26 -4.78 0.09 892 0.27 0.93 0.79 0.82 0.23 1.00
10000 -588.58 -38.97 133.40 533.08 -6.32 -4.89 32.15 826 0.27 0.93 0.79 0.82 0.23 1.00
12000 -583.27 -40.78 175.22 664.15 -6.36 -4.84 -13.44 852 0.26 0.93 0.78 0.82 0.23 1.00
14000 -597.02 -41.23 189.28 677.93 -6.13 -4.62 62.50 894 0.26 0.93 0.78 0.83 0.23 1.00
16000 -588.78 -38.49 133.22 420.87 -6.21 -4.11 97.16 855 0.27 0.93 0.78 0.83 0.23 1.00
18000 -584.75 -39.28 143.07 522.83 -6.41 -4.52 1.94 874 0.26 0.92 0.79 0.83 0.24 1.00
20000 -583.40 -41.06 187.33 642.00 -6.57 -4.67 13.89 867 0.26 0.93 0.79 0.83 0.23 1.00
22000 -597.02 -39.85 150.75 612.46 -6.52 -4.87 -0.83 938 0.26 0.93 0.79 0.83 0.23 1.00
24000 -592.27 -36.09 91.15 308.97 -6.29 -4.87 -19.24 864 0.26 0.93 0.79 0.83 0.23 1.00
26000 -594.73 -37.87 119.58 400.65 -6.42 -4.87 24.79 869 0.25 0.93 0.79 0.83 0.23 1.00
28000 -583.76 -37.74 115.39 408.31 -6.46 -4.76 28.95 849 0.25 0.92 0.79 0.83 0.23 1.00
30000 -584.13 -39.36 145.15 526.49 -6.64 -4.80 28.89 852 0.25 0.92 0.79 0.83 0.23 1.00
32000 -582.61 -39.04 142.30 470.36 -6.52 -4.71 33.03 836 0.25 0.92 0.79 0.83 0.23 1.00
34000 -592.11 -38.49 129.83 446.70 -6.43 -4.84 -29.96 901 0.25 0.92 0.79 0.83 0.23 1.00
36000 -612.74 -34.93 75.46 263.14 -6.29 -4.79 -4.08 853 0.25 0.92 0.79 0.83 0.24 1.00
38000 -591.83 -37.88 116.14 432.02 -6.48 -4.60 28.81 873 0.25 0.92 0.79 0.83 0.24 1.00
40000 -603.99 -39.44 146.89 532.45 -6.39 -4.93 -6.71 861 0.25 0.92 0.79 0.83 0.24 1.00
42000 -586.39 -40.05 161.06 570.30 -6.33 -4.94 16.44 855 0.25 0.92 0.79 0.83 0.24 1.00
44000 -583.98 -38.96 137.79 490.19 -6.57 -4.86 5.83 826 0.25 0.93 0.79 0.82 0.24 1.00
46000 -584.42 -39.59 149.11 551.60 -6.48 -4.84 1.37 826 0.25 0.93 0.79 0.82 0.24 1.00
48000 -597.49 -37.67 111.92 424.51 -6.28 -4.83 -7.83 878 0.25 0.93 0.79 0.82 0.24 1.00
50000 -606.15 -39.68 152.47 545.14 -6.54 -4.73 26.50 853 0.25 0.93 0.79 0.82 0.24 1.00
52000 -583.56 -39.35 148.20 496.32 -6.55 -4.86 26.14 826 0.25 0.93 0.79 0.82 0.24 1.00
54000 -580.89 -42.45 231.74 728.12 -6.53 -4.70 14.94 925 0.25 0.93 0.79 0.82 0.24 1.00
56000 -599.03 -38.91 133.52 515.63 -6.14 -4.79 -19.08 857 0.25 0.93 0.79 0.82 0.24 1.00
58000 -581.65 -40.03 161.01 565.71 -6.43 -4.79 -25.00 925 0.25 0.93 0.79 0.83 0.24 1.00
60000 -576.63 -42.11 220.94 698.27 -6.63 -4.79 -7.62 859 0.25 0.93 0.79 0.82 0.24 1.00
62000 -590.18 -40.45 173.03 580.82 -6.38 -4.79 27.79 868 0.25 0.93 0.79 0.83 0.24 1.00
64000 -594.47 -36.24 89.12 351.08 -6.56 -4.87 -3.13 892 0.25 0.93 0.79 0.83 0.24 1.00
66000 -590.92 -46.53 394.08 1229.26 -6.61 -4.76 17.19 861 0.25 0.93 0.79 0.83 0.24 1.00
68000 -588.47 -37.45 108.85 406.31 -6.62 -4.53 -11.20 826 0.25 0.93 0.79 0.83 0.24 1.00
70000 -589.92 -40.52 168.51 645.45 -6.48 -4.68 -16.62 804 0.25 0.93 0.79 0.82 0.24 1.00
72000 -592.50 -42.38 223.14 777.90 -6.38 -5.03 15.14 865 0.25 0.93 0.79 0.82 0.24 1.00
74000 -590.80 -40.73 174.08 656.37 -6.56 -4.68 33.71 838 0.26 0.93 0.79 0.82 0.24 1.00
76000 -591.83 -38.55 125.51 500.89 -6.43 -4.78 22.66 834 0.25 0.93 0.79 0.82 0.24 1.00
78000 -590.42 -38.57 130.62 459.82 -6.51 -4.79 15.19 838 0.25 0.93 0.79 0.82 0.24 1.00
80000 -583.16 -39.71 157.24 513.15 -6.48 -4.56 33.58 834 0.26 0.93 0.79 0.82 0.24 1.00
82000 -582.41 -39.94 165.13 508.46 -6.57 -4.78 5.07 906 0.26 0.93 0.79 0.82 0.24 1.00
84000 -593.76 -37.34 111.31 363.99 -6.48 -4.96 25.00 838 0.26 0.93 0.79 0.82 0.24 1.00
86000 -588.90 -41.11 186.94 659.90 -6.49 -4.80 28.37 892 0.26 0.93 0.79 0.82 0.24 1.00
88000 -580.48 -42.04 219.29 689.53 -6.45 -4.66 1.63 906 0.26 0.93 0.79 0.82 0.24 1.00
90000 -591.94 -38.19 120.61 460.06 -6.26 -4.70 13.72 892 0.25 0.93 0.79 0.82 0.24 1.00
92000 -593.83 -38.56 129.95 462.93 -6.47 -4.67 7.51 852 0.25 0.93 0.79 0.82 0.24 1.00
94000 -590.57 -39.48 145.92 551.72 -6.45 -4.69 -3.98 865 0.25 0.93 0.79 0.82 0.24 1.00
96000 -589.16 -40.19 167.17 558.78 -6.47 -4.80 -13.93 866 0.26 0.93 0.79 0.82 0.24 1.00
98000 -591.75 -38.85 130.63 527.34 -6.54 -4.82 0.31 859 0.26 0.93 0.79 0.82 0.24 1.00
100000 -592.66 -38.85 136.69 472.15 -6.39 -4.67 -21.49 859 0.25 0.93 0.79 0.82 0.24 1.00 chain.step <- mymcmc.step$load()
chain.step <- set.burnin(chain.step, 0.3)
plot(chain.step)
plot(midbins, startpar.step$theta, ylim=c(-10, 0), type="n")
x <- -350:200
points(td[['tmin.025']], td[['lnVs']], pch=21, bg="gray50")lapply(seq(1, length(chain.step$gen), 10), function(i) lines(x, ifelse(x>chain.step$beta_center[i], chain.step$beta_right[i], chain.step$beta_left[i]), col=makeTransparent(rgb(1, 0, 0), alpha=10)))[[1]]
NULL
[[2]]
NULL
[[3]]
NULL
[[4]]
NULL
[[5]]
NULL
[[6]]
NULL
[[7]]
NULL
[[8]]
NULL
[[9]]
NULL
[[10]]
NULL
[[11]]
NULL
[[12]]
NULL
[[13]]
NULL
[[14]]
NULL
[[15]]
NULL
[[16]]
NULL
[[17]]
NULL
[[18]]
NULL
[[19]]
NULL
[[20]]
NULL
[[21]]
NULL
[[22]]
NULL
[[23]]
NULL
[[24]]
NULL
[[25]]
NULL
[[26]]
NULL
[[27]]
NULL
[[28]]
NULL
[[29]]
NULL
[[30]]
NULL
[[31]]
NULL
[[32]]
NULL
[[33]]
NULL
[[34]]
NULL
[[35]]
NULL
[[36]]
NULL
[[37]]
NULL
[[38]]
NULL
[[39]]
NULL
[[40]]
NULL
[[41]]
NULL
[[42]]
NULL
[[43]]
NULL
[[44]]
NULL
[[45]]
NULL
[[46]]
NULL
[[47]]
NULL
[[48]]
NULL
[[49]]
NULL
[[50]]
NULL
[[51]]
NULL
[[52]]
NULL
[[53]]
NULL
[[54]]
NULL
[[55]]
NULL
[[56]]
NULL
[[57]]
NULL
[[58]]
NULL
[[59]]
NULL
[[60]]
NULL
[[61]]
NULL
[[62]]
NULL
[[63]]
NULL
[[64]]
NULL
[[65]]
NULL
[[66]]
NULL
[[67]]
NULL
[[68]]
NULL
[[69]]
NULL
[[70]]
NULL
[[71]]
NULL
[[72]]
NULL
[[73]]
NULL
[[74]]
NULL
[[75]]
NULL
[[76]]
NULL
[[77]]
NULL
[[78]]
NULL
[[79]]
NULL
[[80]]
NULL
[[81]]
NULL
[[82]]
NULL
[[83]]
NULL
[[84]]
NULL
[[85]]
NULL
[[86]]
NULL
[[87]]
NULL
[[88]]
NULL
[[89]]
NULL
[[90]]
NULL
[[91]]
NULL
[[92]]
NULL
[[93]]
NULL
[[94]]
NULL
[[95]]
NULL
[[96]]
NULL
[[97]]
NULL
[[98]]
NULL
[[99]]
NULL
[[100]]
NULL
[[101]]
NULL
mymcmc.sigmoid$run(100000)gen lnL prior alpha sig2 beta_le beta_ri beta_ce beta_sl k .alpha .beta_c .beta_l .beta_r .beta_s .sig2 .theta
2000 -579.39 -38.65 182.95 611.04 -2.13 -5.92 42.53 0.01 846 0.35 0.72 0.79 0.81 0.49 0.28 1.00
4000 -557.49 -44.44 394.12 1199.24 -2.34 -6.12 17.40 0.01 807 0.31 0.75 0.75 0.82 0.40 0.25 1.00
6000 -570.73 -38.65 184.94 590.22 -2.10 -5.73 61.63 0.01 896 0.30 0.76 0.73 0.82 0.38 0.24 1.00
8000 -577.90 -39.54 214.36 686.73 -2.09 -5.19 85.80 0.01 846 0.28 0.75 0.72 0.81 0.39 0.25 1.00
10000 -566.00 -43.96 371.12 1133.71 -2.59 -5.37 31.63 0.01 935 0.27 0.75 0.73 0.82 0.40 0.25 1.00
12000 -570.57 -40.50 244.09 787.57 -3.18 -4.29 20.82 0.01 841 0.27 0.75 0.72 0.82 0.41 0.25 1.00
14000 -572.54 -38.93 202.08 584.34 -3.44 -4.66 -29.00 0.01 895 0.27 0.74 0.72 0.82 0.43 0.24 1.00
16000 -572.50 -38.42 184.63 548.14 -2.97 -5.07 -5.70 0.01 839 0.26 0.74 0.71 0.82 0.44 0.24 1.00
18000 -570.38 -41.24 250.40 800.82 -2.42 -5.68 57.67 0.01 852 0.26 0.74 0.71 0.82 0.44 0.24 1.00
20000 -568.21 -40.75 246.81 745.01 -2.47 -6.15 -4.04 0.01 925 0.26 0.73 0.71 0.82 0.43 0.24 1.00
22000 -568.20 -36.89 146.95 442.62 -1.99 -6.15 55.13 0.01 867 0.25 0.73 0.70 0.82 0.42 0.25 1.00
24000 -565.80 -40.40 245.53 750.51 -2.67 -5.09 44.04 0.01 855 0.25 0.73 0.71 0.82 0.42 0.24 1.00
26000 -571.11 -43.28 337.83 1181.64 -2.85 -4.92 28.75 0.01 851 0.25 0.74 0.71 0.82 0.42 0.24 1.00
28000 -557.80 -42.24 321.34 860.07 -3.35 -4.87 -22.20 0.01 807 0.26 0.74 0.71 0.82 0.42 0.24 1.00
30000 -557.81 -43.16 374.55 903.59 -2.82 -4.41 61.17 0.01 878 0.26 0.74 0.71 0.82 0.43 0.24 1.00
32000 -572.32 -41.79 285.87 951.22 -2.68 -4.69 48.10 0.01 857 0.26 0.74 0.71 0.82 0.44 0.24 1.00
34000 -567.34 -40.93 258.66 850.16 -3.66 -3.98 -10.38 0.01 887 0.26 0.74 0.71 0.82 0.45 0.24 1.00
36000 -568.31 -41.80 295.74 952.27 -3.82 -3.50 13.83 0.01 826 0.26 0.74 0.71 0.82 0.46 0.24 1.00
38000 -562.73 -37.58 172.88 500.52 -4.21 -3.05 -9.93 0.02 906 0.26 0.74 0.71 0.82 0.47 0.24 1.00
40000 -569.20 -39.07 210.42 633.06 -3.93 -2.83 29.50 0.02 895 0.26 0.74 0.71 0.82 0.49 0.24 1.00
42000 -574.72 -40.30 243.84 783.93 -4.00 -3.03 10.00 0.02 846 0.26 0.74 0.71 0.82 0.50 0.24 1.00
44000 -576.46 -34.75 110.14 367.11 -4.58 -3.19 -52.58 0.02 849 0.26 0.74 0.72 0.82 0.52 0.24 1.00
46000 -579.26 -42.01 304.42 976.10 -4.33 -2.40 20.00 0.02 869 0.26 0.74 0.72 0.82 0.53 0.24 1.00
48000 -570.93 -41.25 279.67 847.94 -3.88 -3.19 -2.91 0.01 866 0.26 0.74 0.72 0.82 0.54 0.25 1.00
50000 -569.75 -39.11 199.65 624.50 -3.06 -4.78 11.91 0.01 868 0.25 0.74 0.72 0.82 0.54 0.25 1.00
52000 -571.94 -43.49 364.57 1114.82 -3.54 -3.86 21.37 0.01 866 0.25 0.74 0.71 0.82 0.54 0.24 1.00
54000 -574.76 -38.59 187.01 618.30 -2.94 -4.78 8.49 0.01 851 0.25 0.74 0.71 0.82 0.54 0.25 1.00
56000 -579.37 -37.03 143.30 538.58 -3.62 -4.70 -49.23 0.01 854 0.25 0.75 0.72 0.82 0.54 0.25 1.00
58000 -565.39 -39.86 232.52 673.89 -3.40 -3.94 29.58 0.01 836 0.25 0.74 0.72 0.82 0.54 0.25 1.00
60000 -563.78 -39.90 236.18 631.31 -2.88 -5.03 26.07 0.01 873 0.25 0.74 0.72 0.82 0.54 0.25 1.00
62000 -555.36 -41.65 303.76 825.78 -3.59 -3.37 31.83 0.01 836 0.25 0.74 0.72 0.82 0.54 0.25 1.00
64000 -566.36 -38.48 191.88 545.73 -3.71 -4.06 -14.18 0.01 874 0.25 0.74 0.72 0.82 0.55 0.25 1.00
66000 -578.59 -38.96 196.95 677.28 -3.58 -3.62 24.24 0.01 869 0.25 0.74 0.72 0.82 0.55 0.25 1.00
68000 -579.21 -37.27 165.59 480.62 -4.45 -2.57 15.60 0.02 825 0.25 0.74 0.72 0.82 0.56 0.25 1.00
70000 -571.85 -41.43 282.85 910.70 -4.37 -2.75 -9.26 0.02 838 0.25 0.75 0.72 0.82 0.56 0.24 1.00
72000 -570.63 -39.79 229.36 654.21 -3.52 -3.95 13.79 0.01 810 0.25 0.75 0.72 0.82 0.57 0.24 1.00
74000 -558.05 -37.51 172.64 486.45 -3.60 -3.58 25.53 0.02 836 0.25 0.74 0.72 0.82 0.57 0.24 1.00
76000 -568.03 -36.78 153.02 455.04 -3.77 -3.57 5.19 0.01 879 0.25 0.74 0.72 0.82 0.57 0.24 1.00
78000 -569.78 -40.68 248.48 842.61 -3.78 -3.64 14.95 0.01 834 0.25 0.74 0.72 0.82 0.57 0.25 1.00
80000 -573.10 -39.64 228.50 673.36 -4.25 -2.86 -14.10 0.02 879 0.25 0.74 0.72 0.82 0.58 0.25 1.00
82000 -559.90 -38.89 204.63 602.05 -4.01 -3.61 -21.88 0.01 818 0.25 0.74 0.72 0.82 0.59 0.25 1.00
84000 -569.71 -37.99 179.09 565.55 -4.38 -2.75 -5.22 0.02 925 0.25 0.74 0.72 0.82 0.59 0.25 1.00
86000 -555.41 -37.12 156.58 509.35 -4.10 -3.57 -27.69 0.01 788 0.25 0.74 0.72 0.82 0.59 0.24 1.00
88000 -567.70 -40.01 242.16 675.87 -3.98 -3.18 4.88 0.01 925 0.25 0.75 0.72 0.82 0.60 0.24 1.00
90000 -578.71 -41.73 280.42 954.27 -3.16 -4.54 7.05 0.01 859 0.25 0.75 0.72 0.82 0.60 0.25 1.00
92000 -566.31 -43.36 347.88 1224.21 -3.53 -4.02 3.51 0.01 835 0.25 0.74 0.72 0.82 0.60 0.25 1.00
94000 -574.04 -40.55 250.45 742.14 -3.11 -4.13 49.77 0.01 897 0.25 0.75 0.72 0.82 0.60 0.25 1.00
96000 -578.37 -38.35 180.00 578.34 -3.83 -4.68 -76.81 0.01 882 0.25 0.75 0.72 0.82 0.60 0.25 1.00
98000 -568.41 -41.33 276.13 822.04 -2.95 -4.92 16.90 0.01 810 0.25 0.75 0.72 0.82 0.60 0.25 1.00
100000 -573.86 -45.99 488.74 1490.31 -2.78 -4.53 73.80 0.01 875 0.25 0.75 0.72 0.82 0.59 0.25 1.00 chain.sigmoid <- mymcmc.sigmoid$load()
chain.sigmoid <- set.burnin(chain.sigmoid, 0.3)
plot(chain.sigmoid)
plot(midbins, startpar.step$theta, ylim=c(-10, 0), type="n")
x <- -350:200
points(td[['tmin.025']], td[['lnVs']], pch=21, bg="gray50")lapply(seq(1, length(chain.sigmoid$gen), 1), function(i) lines(x, chain.sigmoid$beta_left[i]+chain.sigmoid$beta_right[i]/(1 + exp(chain.sigmoid$beta_slope[i]*(x-chain.sigmoid$beta_center[i]))), col=makeTransparent(rgb(1, 0, 0), alpha=10)))[[1]]
NULL
[[2]]
NULL
[[3]]
NULL
[[4]]
NULL
[[5]]
NULL
[[6]]
NULL
[[7]]
NULL
[[8]]
NULL
[[9]]
NULL
[[10]]
NULL
[[11]]
NULL
[[12]]
NULL
[[13]]
NULL
[[14]]
NULL
[[15]]
NULL
[[16]]
NULL
[[17]]
NULL
[[18]]
NULL
[[19]]
NULL
[[20]]
NULL
[[21]]
NULL
[[22]]
NULL
[[23]]
NULL
[[24]]
NULL
[[25]]
NULL
[[26]]
NULL
[[27]]
NULL
[[28]]
NULL
[[29]]
NULL
[[30]]
NULL
[[31]]
NULL
[[32]]
NULL
[[33]]
NULL
[[34]]
NULL
[[35]]
NULL
[[36]]
NULL
[[37]]
NULL
[[38]]
NULL
[[39]]
NULL
[[40]]
NULL
[[41]]
NULL
[[42]]
NULL
[[43]]
NULL
[[44]]
NULL
[[45]]
NULL
[[46]]
NULL
[[47]]
NULL
[[48]]
NULL
[[49]]
NULL
[[50]]
NULL
[[51]]
NULL
[[52]]
NULL
[[53]]
NULL
[[54]]
NULL
[[55]]
NULL
[[56]]
NULL
[[57]]
NULL
[[58]]
NULL
[[59]]
NULL
[[60]]
NULL
[[61]]
NULL
[[62]]
NULL
[[63]]
NULL
[[64]]
NULL
[[65]]
NULL
[[66]]
NULL
[[67]]
NULL
[[68]]
NULL
[[69]]
NULL
[[70]]
NULL
[[71]]
NULL
[[72]]
NULL
[[73]]
NULL
[[74]]
NULL
[[75]]
NULL
[[76]]
NULL
[[77]]
NULL
[[78]]
NULL
[[79]]
NULL
[[80]]
NULL
[[81]]
NULL
[[82]]
NULL
[[83]]
NULL
[[84]]
NULL
[[85]]
NULL
[[86]]
NULL
[[87]]
NULL
[[88]]
NULL
[[89]]
NULL
[[90]]
NULL
[[91]]
NULL
[[92]]
NULL
[[93]]
NULL
[[94]]
NULL
[[95]]
NULL
[[96]]
NULL
[[97]]
NULL
[[98]]
NULL
[[99]]
NULL
[[100]]
NULL
[[101]]
NULL
[[102]]
NULL
[[103]]
NULL
[[104]]
NULL
[[105]]
NULL
[[106]]
NULL
[[107]]
NULL
[[108]]
NULL
[[109]]
NULL
[[110]]
NULL
[[111]]
NULL
[[112]]
NULL
[[113]]
NULL
[[114]]
NULL
[[115]]
NULL
[[116]]
NULL
[[117]]
NULL
[[118]]
NULL
[[119]]
NULL
[[120]]
NULL
[[121]]
NULL
[[122]]
NULL
[[123]]
NULL
[[124]]
NULL
[[125]]
NULL
[[126]]
NULL
[[127]]
NULL
[[128]]
NULL
[[129]]
NULL
[[130]]
NULL
[[131]]
NULL
[[132]]
NULL
[[133]]
NULL
[[134]]
NULL
[[135]]
NULL
[[136]]
NULL
[[137]]
NULL
[[138]]
NULL
[[139]]
NULL
[[140]]
NULL
[[141]]
NULL
[[142]]
NULL
[[143]]
NULL
[[144]]
NULL
[[145]]
NULL
[[146]]
NULL
[[147]]
NULL
[[148]]
NULL
[[149]]
NULL
[[150]]
NULL
[[151]]
NULL
[[152]]
NULL
[[153]]
NULL
[[154]]
NULL
[[155]]
NULL
[[156]]
NULL
[[157]]
NULL
[[158]]
NULL
[[159]]
NULL
[[160]]
NULL
[[161]]
NULL
[[162]]
NULL
[[163]]
NULL
[[164]]
NULL
[[165]]
NULL
[[166]]
NULL
[[167]]
NULL
[[168]]
NULL
[[169]]
NULL
[[170]]
NULL
[[171]]
NULL
[[172]]
NULL
[[173]]
NULL
[[174]]
NULL
[[175]]
NULL
[[176]]
NULL
[[177]]
NULL
[[178]]
NULL
[[179]]
NULL
[[180]]
NULL
[[181]]
NULL
[[182]]
NULL
[[183]]
NULL
[[184]]
NULL
[[185]]
NULL
[[186]]
NULL
[[187]]
NULL
[[188]]
NULL
[[189]]
NULL
[[190]]
NULL
[[191]]
NULL
[[192]]
NULL
[[193]]
NULL
[[194]]
NULL
[[195]]
NULL
[[196]]
NULL
[[197]]
NULL
[[198]]
NULL
[[199]]
NULL
[[200]]
NULL
[[201]]
NULL
[[202]]
NULL
[[203]]
NULL
[[204]]
NULL
[[205]]
NULL
[[206]]
NULL
[[207]]
NULL
[[208]]
NULL
[[209]]
NULL
[[210]]
NULL
[[211]]
NULL
[[212]]
NULL
[[213]]
NULL
[[214]]
NULL
[[215]]
NULL
[[216]]
NULL
[[217]]
NULL
[[218]]
NULL
[[219]]
NULL
[[220]]
NULL
[[221]]
NULL
[[222]]
NULL
[[223]]
NULL
[[224]]
NULL
[[225]]
NULL
[[226]]
NULL
[[227]]
NULL
[[228]]
NULL
[[229]]
NULL
[[230]]
NULL
[[231]]
NULL
[[232]]
NULL
[[233]]
NULL
[[234]]
NULL
[[235]]
NULL
[[236]]
NULL
[[237]]
NULL
[[238]]
NULL
[[239]]
NULL
[[240]]
NULL
[[241]]
NULL
[[242]]
NULL
[[243]]
NULL
[[244]]
NULL
[[245]]
NULL
[[246]]
NULL
[[247]]
NULL
[[248]]
NULL
[[249]]
NULL
[[250]]
NULL
[[251]]
NULL
[[252]]
NULL
[[253]]
NULL
[[254]]
NULL
[[255]]
NULL
[[256]]
NULL
[[257]]
NULL
[[258]]
NULL
[[259]]
NULL
[[260]]
NULL
[[261]]
NULL
[[262]]
NULL
[[263]]
NULL
[[264]]
NULL
[[265]]
NULL
[[266]]
NULL
[[267]]
NULL
[[268]]
NULL
[[269]]
NULL
[[270]]
NULL
[[271]]
NULL
[[272]]
NULL
[[273]]
NULL
[[274]]
NULL
[[275]]
NULL
[[276]]
NULL
[[277]]
NULL
[[278]]
NULL
[[279]]
NULL
[[280]]
NULL
[[281]]
NULL
[[282]]
NULL
[[283]]
NULL
[[284]]
NULL
[[285]]
NULL
[[286]]
NULL
[[287]]
NULL
[[288]]
NULL
[[289]]
NULL
[[290]]
NULL
[[291]]
NULL
[[292]]
NULL
[[293]]
NULL
[[294]]
NULL
[[295]]
NULL
[[296]]
NULL
[[297]]
NULL
[[298]]
NULL
[[299]]
NULL
[[300]]
NULL
[[301]]
NULL
[[302]]
NULL
[[303]]
NULL
[[304]]
NULL
[[305]]
NULL
[[306]]
NULL
[[307]]
NULL
[[308]]
NULL
[[309]]
NULL
[[310]]
NULL
[[311]]
NULL
[[312]]
NULL
[[313]]
NULL
[[314]]
NULL
[[315]]
NULL
[[316]]
NULL
[[317]]
NULL
[[318]]
NULL
[[319]]
NULL
[[320]]
NULL
[[321]]
NULL
[[322]]
NULL
[[323]]
NULL
[[324]]
NULL
[[325]]
NULL
[[326]]
NULL
[[327]]
NULL
[[328]]
NULL
[[329]]
NULL
[[330]]
NULL
[[331]]
NULL
[[332]]
NULL
[[333]]
NULL
[[334]]
NULL
[[335]]
NULL
[[336]]
NULL
[[337]]
NULL
[[338]]
NULL
[[339]]
NULL
[[340]]
NULL
[[341]]
NULL
[[342]]
NULL
[[343]]
NULL
[[344]]
NULL
[[345]]
NULL
[[346]]
NULL
[[347]]
NULL
[[348]]
NULL
[[349]]
NULL
[[350]]
NULL
[[351]]
NULL
[[352]]
NULL
[[353]]
NULL
[[354]]
NULL
[[355]]
NULL
[[356]]
NULL
[[357]]
NULL
[[358]]
NULL
[[359]]
NULL
[[360]]
NULL
[[361]]
NULL
[[362]]
NULL
[[363]]
NULL
[[364]]
NULL
[[365]]
NULL
[[366]]
NULL
[[367]]
NULL
[[368]]
NULL
[[369]]
NULL
[[370]]
NULL
[[371]]
NULL
[[372]]
NULL
[[373]]
NULL
[[374]]
NULL
[[375]]
NULL
[[376]]
NULL
[[377]]
NULL
[[378]]
NULL
[[379]]
NULL
[[380]]
NULL
[[381]]
NULL
[[382]]
NULL
[[383]]
NULL
[[384]]
NULL
[[385]]
NULL
[[386]]
NULL
[[387]]
NULL
[[388]]
NULL
[[389]]
NULL
[[390]]
NULL
[[391]]
NULL
[[392]]
NULL
[[393]]
NULL
[[394]]
NULL
[[395]]
NULL
[[396]]
NULL
[[397]]
NULL
[[398]]
NULL
[[399]]
NULL
[[400]]
NULL
[[401]]
NULL
[[402]]
NULL
[[403]]
NULL
[[404]]
NULL
[[405]]
NULL
[[406]]
NULL
[[407]]
NULL
[[408]]
NULL
[[409]]
NULL
[[410]]
NULL
[[411]]
NULL
[[412]]
NULL
[[413]]
NULL
[[414]]
NULL
[[415]]
NULL
[[416]]
NULL
[[417]]
NULL
[[418]]
NULL
[[419]]
NULL
[[420]]
NULL
[[421]]
NULL
[[422]]
NULL
[[423]]
NULL
[[424]]
NULL
[[425]]
NULL
[[426]]
NULL
[[427]]
NULL
[[428]]
NULL
[[429]]
NULL
[[430]]
NULL
[[431]]
NULL
[[432]]
NULL
[[433]]
NULL
[[434]]
NULL
[[435]]
NULL
[[436]]
NULL
[[437]]
NULL
[[438]]
NULL
[[439]]
NULL
[[440]]
NULL
[[441]]
NULL
[[442]]
NULL
[[443]]
NULL
[[444]]
NULL
[[445]]
NULL
[[446]]
NULL
[[447]]
NULL
[[448]]
NULL
[[449]]
NULL
[[450]]
NULL
[[451]]
NULL
[[452]]
NULL
[[453]]
NULL
[[454]]
NULL
[[455]]
NULL
[[456]]
NULL
[[457]]
NULL
[[458]]
NULL
[[459]]
NULL
[[460]]
NULL
[[461]]
NULL
[[462]]
NULL
[[463]]
NULL
[[464]]
NULL
[[465]]
NULL
[[466]]
NULL
[[467]]
NULL
[[468]]
NULL
[[469]]
NULL
[[470]]
NULL
[[471]]
NULL
[[472]]
NULL
[[473]]
NULL
[[474]]
NULL
[[475]]
NULL
[[476]]
NULL
[[477]]
NULL
[[478]]
NULL
[[479]]
NULL
[[480]]
NULL
[[481]]
NULL
[[482]]
NULL
[[483]]
NULL
[[484]]
NULL
[[485]]
NULL
[[486]]
NULL
[[487]]
NULL
[[488]]
NULL
[[489]]
NULL
[[490]]
NULL
[[491]]
NULL
[[492]]
NULL
[[493]]
NULL
[[494]]
NULL
[[495]]
NULL
[[496]]
NULL
[[497]]
NULL
[[498]]
NULL
[[499]]
NULL
[[500]]
NULL
[[501]]
NULL
[[502]]
NULL
[[503]]
NULL
[[504]]
NULL
[[505]]
NULL
[[506]]
NULL
[[507]]
NULL
[[508]]
NULL
[[509]]
NULL
[[510]]
NULL
[[511]]
NULL
[[512]]
NULL
[[513]]
NULL
[[514]]
NULL
[[515]]
NULL
[[516]]
NULL
[[517]]
NULL
[[518]]
NULL
[[519]]
NULL
[[520]]
NULL
[[521]]
NULL
[[522]]
NULL
[[523]]
NULL
[[524]]
NULL
[[525]]
NULL
[[526]]
NULL
[[527]]
NULL
[[528]]
NULL
[[529]]
NULL
[[530]]
NULL
[[531]]
NULL
[[532]]
NULL
[[533]]
NULL
[[534]]
NULL
[[535]]
NULL
[[536]]
NULL
[[537]]
NULL
[[538]]
NULL
[[539]]
NULL
[[540]]
NULL
[[541]]
NULL
[[542]]
NULL
[[543]]
NULL
[[544]]
NULL
[[545]]
NULL
[[546]]
NULL
[[547]]
NULL
[[548]]
NULL
[[549]]
NULL
[[550]]
NULL
[[551]]
NULL
[[552]]
NULL
[[553]]
NULL
[[554]]
NULL
[[555]]
NULL
[[556]]
NULL
[[557]]
NULL
[[558]]
NULL
[[559]]
NULL
[[560]]
NULL
[[561]]
NULL
[[562]]
NULL
[[563]]
NULL
[[564]]
NULL
[[565]]
NULL
[[566]]
NULL
[[567]]
NULL
[[568]]
NULL
[[569]]
NULL
[[570]]
NULL
[[571]]
NULL
[[572]]
NULL
[[573]]
NULL
[[574]]
NULL
[[575]]
NULL
[[576]]
NULL
[[577]]
NULL
[[578]]
NULL
[[579]]
NULL
[[580]]
NULL
[[581]]
NULL
[[582]]
NULL
[[583]]
NULL
[[584]]
NULL
[[585]]
NULL
[[586]]
NULL
[[587]]
NULL
[[588]]
NULL
[[589]]
NULL
[[590]]
NULL
[[591]]
NULL
[[592]]
NULL
[[593]]
NULL
[[594]]
NULL
[[595]]
NULL
[[596]]
NULL
[[597]]
NULL
[[598]]
NULL
[[599]]
NULL
[[600]]
NULL
[[601]]
NULL
[[602]]
NULL
[[603]]
NULL
[[604]]
NULL
[[605]]
NULL
[[606]]
NULL
[[607]]
NULL
[[608]]
NULL
[[609]]
NULL
[[610]]
NULL
[[611]]
NULL
[[612]]
NULL
[[613]]
NULL
[[614]]
NULL
[[615]]
NULL
[[616]]
NULL
[[617]]
NULL
[[618]]
NULL
[[619]]
NULL
[[620]]
NULL
[[621]]
NULL
[[622]]
NULL
[[623]]
NULL
[[624]]
NULL
[[625]]
NULL
[[626]]
NULL
[[627]]
NULL
[[628]]
NULL
[[629]]
NULL
[[630]]
NULL
[[631]]
NULL
[[632]]
NULL
[[633]]
NULL
[[634]]
NULL
[[635]]
NULL
[[636]]
NULL
[[637]]
NULL
[[638]]
NULL
[[639]]
NULL
[[640]]
NULL
[[641]]
NULL
[[642]]
NULL
[[643]]
NULL
[[644]]
NULL
[[645]]
NULL
[[646]]
NULL
[[647]]
NULL
[[648]]
NULL
[[649]]
NULL
[[650]]
NULL
[[651]]
NULL
[[652]]
NULL
[[653]]
NULL
[[654]]
NULL
[[655]]
NULL
[[656]]
NULL
[[657]]
NULL
[[658]]
NULL
[[659]]
NULL
[[660]]
NULL
[[661]]
NULL
[[662]]
NULL
[[663]]
NULL
[[664]]
NULL
[[665]]
NULL
[[666]]
NULL
[[667]]
NULL
[[668]]
NULL
[[669]]
NULL
[[670]]
NULL
[[671]]
NULL
[[672]]
NULL
[[673]]
NULL
[[674]]
NULL
[[675]]
NULL
[[676]]
NULL
[[677]]
NULL
[[678]]
NULL
[[679]]
NULL
[[680]]
NULL
[[681]]
NULL
[[682]]
NULL
[[683]]
NULL
[[684]]
NULL
[[685]]
NULL
[[686]]
NULL
[[687]]
NULL
[[688]]
NULL
[[689]]
NULL
[[690]]
NULL
[[691]]
NULL
[[692]]
NULL
[[693]]
NULL
[[694]]
NULL
[[695]]
NULL
[[696]]
NULL
[[697]]
NULL
[[698]]
NULL
[[699]]
NULL
[[700]]
NULL
[[701]]
NULL
[[702]]
NULL
[[703]]
NULL
[[704]]
NULL
[[705]]
NULL
[[706]]
NULL
[[707]]
NULL
[[708]]
NULL
[[709]]
NULL
[[710]]
NULL
[[711]]
NULL
[[712]]
NULL
[[713]]
NULL
[[714]]
NULL
[[715]]
NULL
[[716]]
NULL
[[717]]
NULL
[[718]]
NULL
[[719]]
NULL
[[720]]
NULL
[[721]]
NULL
[[722]]
NULL
[[723]]
NULL
[[724]]
NULL
[[725]]
NULL
[[726]]
NULL
[[727]]
NULL
[[728]]
NULL
[[729]]
NULL
[[730]]
NULL
[[731]]
NULL
[[732]]
NULL
[[733]]
NULL
[[734]]
NULL
[[735]]
NULL
[[736]]
NULL
[[737]]
NULL
[[738]]
NULL
[[739]]
NULL
[[740]]
NULL
[[741]]
NULL
[[742]]
NULL
[[743]]
NULL
[[744]]
NULL
[[745]]
NULL
[[746]]
NULL
[[747]]
NULL
[[748]]
NULL
[[749]]
NULL
[[750]]
NULL
[[751]]
NULL
[[752]]
NULL
[[753]]
NULL
[[754]]
NULL
[[755]]
NULL
[[756]]
NULL
[[757]]
NULL
[[758]]
NULL
[[759]]
NULL
[[760]]
NULL
[[761]]
NULL
[[762]]
NULL
[[763]]
NULL
[[764]]
NULL
[[765]]
NULL
[[766]]
NULL
[[767]]
NULL
[[768]]
NULL
[[769]]
NULL
[[770]]
NULL
[[771]]
NULL
[[772]]
NULL
[[773]]
NULL
[[774]]
NULL
[[775]]
NULL
[[776]]
NULL
[[777]]
NULL
[[778]]
NULL
[[779]]
NULL
[[780]]
NULL
[[781]]
NULL
[[782]]
NULL
[[783]]
NULL
[[784]]
NULL
[[785]]
NULL
[[786]]
NULL
[[787]]
NULL
[[788]]
NULL
[[789]]
NULL
[[790]]
NULL
[[791]]
NULL
[[792]]
NULL
[[793]]
NULL
[[794]]
NULL
[[795]]
NULL
[[796]]
NULL
[[797]]
NULL
[[798]]
NULL
[[799]]
NULL
[[800]]
NULL
[[801]]
NULL
[[802]]
NULL
[[803]]
NULL
[[804]]
NULL
[[805]]
NULL
[[806]]
NULL
[[807]]
NULL
[[808]]
NULL
[[809]]
NULL
[[810]]
NULL
[[811]]
NULL
[[812]]
NULL
[[813]]
NULL
[[814]]
NULL
[[815]]
NULL
[[816]]
NULL
[[817]]
NULL
[[818]]
NULL
[[819]]
NULL
[[820]]
NULL
[[821]]
NULL
[[822]]
NULL
[[823]]
NULL
[[824]]
NULL
[[825]]
NULL
[[826]]
NULL
[[827]]
NULL
[[828]]
NULL
[[829]]
NULL
[[830]]
NULL
[[831]]
NULL
[[832]]
NULL
[[833]]
NULL
[[834]]
NULL
[[835]]
NULL
[[836]]
NULL
[[837]]
NULL
[[838]]
NULL
[[839]]
NULL
[[840]]
NULL
[[841]]
NULL
[[842]]
NULL
[[843]]
NULL
[[844]]
NULL
[[845]]
NULL
[[846]]
NULL
[[847]]
NULL
[[848]]
NULL
[[849]]
NULL
[[850]]
NULL
[[851]]
NULL
[[852]]
NULL
[[853]]
NULL
[[854]]
NULL
[[855]]
NULL
[[856]]
NULL
[[857]]
NULL
[[858]]
NULL
[[859]]
NULL
[[860]]
NULL
[[861]]
NULL
[[862]]
NULL
[[863]]
NULL
[[864]]
NULL
[[865]]
NULL
[[866]]
NULL
[[867]]
NULL
[[868]]
NULL
[[869]]
NULL
[[870]]
NULL
[[871]]
NULL
[[872]]
NULL
[[873]]
NULL
[[874]]
NULL
[[875]]
NULL
[[876]]
NULL
[[877]]
NULL
[[878]]
NULL
[[879]]
NULL
[[880]]
NULL
[[881]]
NULL
[[882]]
NULL
[[883]]
NULL
[[884]]
NULL
[[885]]
NULL
[[886]]
NULL
[[887]]
NULL
[[888]]
NULL
[[889]]
NULL
[[890]]
NULL
[[891]]
NULL
[[892]]
NULL
[[893]]
NULL
[[894]]
NULL
[[895]]
NULL
[[896]]
NULL
[[897]]
NULL
[[898]]
NULL
[[899]]
NULL
[[900]]
NULL
[[901]]
NULL
[[902]]
NULL
[[903]]
NULL
[[904]]
NULL
[[905]]
NULL
[[906]]
NULL
[[907]]
NULL
[[908]]
NULL
[[909]]
NULL
[[910]]
NULL
[[911]]
NULL
[[912]]
NULL
[[913]]
NULL
[[914]]
NULL
[[915]]
NULL
[[916]]
NULL
[[917]]
NULL
[[918]]
NULL
[[919]]
NULL
[[920]]
NULL
[[921]]
NULL
[[922]]
NULL
[[923]]
NULL
[[924]]
NULL
[[925]]
NULL
[[926]]
NULL
[[927]]
NULL
[[928]]
NULL
[[929]]
NULL
[[930]]
NULL
[[931]]
NULL
[[932]]
NULL
[[933]]
NULL
[[934]]
NULL
[[935]]
NULL
[[936]]
NULL
[[937]]
NULL
[[938]]
NULL
[[939]]
NULL
[[940]]
NULL
[[941]]
NULL
[[942]]
NULL
[[943]]
NULL
[[944]]
NULL
[[945]]
NULL
[[946]]
NULL
[[947]]
NULL
[[948]]
NULL
[[949]]
NULL
[[950]]
NULL
[[951]]
NULL
[[952]]
NULL
[[953]]
NULL
[[954]]
NULL
[[955]]
NULL
[[956]]
NULL
[[957]]
NULL
[[958]]
NULL
[[959]]
NULL
[[960]]
NULL
[[961]]
NULL
[[962]]
NULL
[[963]]
NULL
[[964]]
NULL
[[965]]
NULL
[[966]]
NULL
[[967]]
NULL
[[968]]
NULL
[[969]]
NULL
[[970]]
NULL
[[971]]
NULL
[[972]]
NULL
[[973]]
NULL
[[974]]
NULL
[[975]]
NULL
[[976]]
NULL
[[977]]
NULL
[[978]]
NULL
[[979]]
NULL
[[980]]
NULL
[[981]]
NULL
[[982]]
NULL
[[983]]
NULL
[[984]]
NULL
[[985]]
NULL
[[986]]
NULL
[[987]]
NULL
[[988]]
NULL
[[989]]
NULL
[[990]]
NULL
[[991]]
NULL
[[992]]
NULL
[[993]]
NULL
[[994]]
NULL
[[995]]
NULL
[[996]]
NULL
[[997]]
NULL
[[998]]
NULL
[[999]]
NULL
[[1000]]
NULL
[[1001]]
NULL
mymcmc.linear$run(100000)gen lnL prior alpha sig2 beta_in beta_sl k .alpha .beta_i .beta_s .sig2 .theta
2000 -572.10 -29.46 158.75 506.01 -5.53 0.01 811 0.35 0.50 0.47 0.26 1.00
4000 -570.47 -37.62 481.10 1325.13 -5.50 0.01 858 0.30 0.45 0.46 0.25 1.00
6000 -576.73 -30.28 173.30 615.18 -5.65 0.01 859 0.27 0.43 0.44 0.24 1.00
8000 -572.35 -31.74 212.98 743.03 -5.66 0.01 866 0.26 0.43 0.44 0.25 1.00
10000 -560.90 -33.58 282.48 839.97 -5.45 0.01 863 0.26 0.43 0.44 0.25 1.00
12000 -571.84 -36.49 415.77 1181.50 -5.54 0.01 838 0.25 0.43 0.43 0.25 1.00
14000 -561.23 -31.69 217.92 677.45 -5.58 0.01 851 0.25 0.43 0.42 0.25 1.00
16000 -573.89 -29.52 157.71 536.20 -5.64 0.01 896 0.25 0.43 0.42 0.24 1.00
18000 -569.20 -27.58 117.45 412.34 -5.50 0.01 849 0.26 0.43 0.42 0.24 1.00
20000 -573.08 -31.78 213.79 753.12 -5.66 0.01 804 0.26 0.43 0.42 0.25 1.00
22000 -577.22 -30.56 185.56 585.96 -5.50 0.01 866 0.26 0.43 0.42 0.25 1.00
24000 -562.03 -29.33 156.51 490.26 -5.52 0.01 818 0.26 0.44 0.42 0.24 1.00
26000 -567.11 -30.92 202.55 561.21 -5.59 0.01 877 0.26 0.43 0.42 0.25 1.00
28000 -578.43 -30.63 189.43 574.16 -5.51 0.01 867 0.26 0.43 0.42 0.24 1.00
30000 -568.75 -32.85 263.18 725.16 -5.62 0.01 866 0.26 0.43 0.42 0.25 1.00
32000 -571.49 -35.74 367.99 1170.33 -5.57 0.01 876 0.26 0.43 0.41 0.25 1.00
34000 -573.84 -28.03 137.66 351.82 -5.50 0.01 867 0.26 0.43 0.41 0.25 1.00
36000 -575.74 -34.09 296.30 962.45 -5.60 0.01 868 0.26 0.43 0.41 0.25 1.00
38000 -571.84 -31.04 202.19 597.22 -5.55 0.01 858 0.26 0.43 0.41 0.25 1.00
40000 -577.03 -31.61 207.97 741.22 -5.63 0.01 866 0.26 0.43 0.41 0.25 1.00
42000 -579.65 -28.25 131.47 436.53 -5.42 0.01 875 0.26 0.43 0.41 0.25 1.00
44000 -574.60 -32.84 243.39 891.08 -5.56 0.01 878 0.26 0.43 0.41 0.25 1.00
46000 -565.61 -33.11 263.26 810.82 -5.46 0.01 874 0.26 0.43 0.41 0.25 1.00
48000 -581.27 -29.16 156.16 462.74 -5.79 0.01 871 0.26 0.43 0.41 0.25 1.00
50000 -570.69 -29.89 172.00 515.81 -5.61 0.01 925 0.25 0.43 0.41 0.25 1.00
52000 -578.44 -31.12 194.53 676.49 -5.42 0.01 897 0.26 0.43 0.41 0.25 1.00
54000 -570.78 -29.75 166.21 522.91 -5.57 0.01 925 0.25 0.43 0.41 0.25 1.00
56000 -576.91 -32.46 246.74 707.35 -5.53 0.01 861 0.25 0.43 0.41 0.25 1.00
58000 -574.94 -30.98 196.68 627.54 -5.59 0.01 868 0.26 0.43 0.41 0.25 1.00
60000 -573.95 -30.97 198.70 608.13 -5.64 0.01 838 0.26 0.43 0.41 0.25 1.00
62000 -569.48 -33.97 292.57 937.03 -5.58 0.01 811 0.26 0.43 0.41 0.25 1.00
64000 -584.18 -30.13 179.96 519.31 -5.68 0.01 869 0.25 0.43 0.41 0.25 1.00
66000 -565.43 -31.99 229.64 683.73 -5.57 0.01 839 0.25 0.42 0.41 0.25 1.00
68000 -568.29 -38.68 541.78 1557.41 -5.52 0.01 847 0.25 0.42 0.41 0.25 1.00
70000 -580.56 -31.82 210.86 783.44 -5.48 0.01 860 0.25 0.43 0.41 0.25 1.00
72000 -565.98 -28.59 137.80 468.08 -5.55 0.01 836 0.25 0.43 0.41 0.25 1.00
74000 -567.81 -33.70 297.87 781.20 -5.60 0.01 879 0.25 0.43 0.41 0.25 1.00
76000 -575.76 -33.98 285.42 1019.56 -5.66 0.01 854 0.25 0.43 0.41 0.25 1.00
78000 -573.31 -32.87 257.81 767.95 -5.54 0.01 838 0.25 0.43 0.41 0.25 1.00
80000 -564.28 -35.08 351.86 964.50 -5.60 0.01 849 0.25 0.42 0.41 0.25 1.00
82000 -579.53 -28.43 138.87 429.24 -5.74 0.01 888 0.25 0.42 0.41 0.25 1.00
84000 -565.66 -32.66 240.23 838.26 -5.48 0.01 873 0.25 0.42 0.41 0.25 1.00
86000 -573.71 -28.59 136.79 476.51 -5.57 0.01 857 0.25 0.43 0.41 0.25 1.00
88000 -576.16 -31.93 225.35 698.42 -5.57 0.01 842 0.25 0.43 0.41 0.25 1.00
90000 -574.92 -31.29 206.03 632.96 -5.40 0.01 851 0.25 0.43 0.41 0.25 1.00
92000 -571.82 -28.29 139.53 390.61 -5.54 0.01 874 0.25 0.42 0.41 0.25 1.00
94000 -576.39 -31.20 201.83 654.59 -5.63 0.01 842 0.25 0.43 0.41 0.25 1.00
96000 -575.75 -30.10 165.41 626.99 -5.50 0.01 896 0.25 0.43 0.41 0.25 1.00
98000 -586.96 -28.71 145.72 437.10 -5.68 0.01 853 0.25 0.43 0.41 0.24 1.00
100000 -572.65 -33.31 266.43 877.81 -5.56 0.01 876 0.25 0.43 0.41 0.25 1.00 chain.linear <- mymcmc.linear$load()
chain.linear <- set.burnin(chain.linear, 0.3)
plot(chain.linear)
plot(midbins, startpar.linear$theta, ylim=c(-10, 0), type="n")
x <- -350:200
points(td[['tmin.025']], td[['lnVs']], pch=21, bg="gray50")lapply(seq(1, length(chain.linear$gen), 1), function(i) lines(x, chain.linear$beta_intercept[i]+chain.linear$beta_slope[i]*x, col=makeTransparent(rgb(1, 0, 0), alpha=10)))[[1]]
NULL
[[2]]
NULL
[[3]]
NULL
[[4]]
NULL
[[5]]
NULL
[[6]]
NULL
[[7]]
NULL
[[8]]
NULL
[[9]]
NULL
[[10]]
NULL
[[11]]
NULL
[[12]]
NULL
[[13]]
NULL
[[14]]
NULL
[[15]]
NULL
[[16]]
NULL
[[17]]
NULL
[[18]]
NULL
[[19]]
NULL
[[20]]
NULL
[[21]]
NULL
[[22]]
NULL
[[23]]
NULL
[[24]]
NULL
[[25]]
NULL
[[26]]
NULL
[[27]]
NULL
[[28]]
NULL
[[29]]
NULL
[[30]]
NULL
[[31]]
NULL
[[32]]
NULL
[[33]]
NULL
[[34]]
NULL
[[35]]
NULL
[[36]]
NULL
[[37]]
NULL
[[38]]
NULL
[[39]]
NULL
[[40]]
NULL
[[41]]
NULL
[[42]]
NULL
[[43]]
NULL
[[44]]
NULL
[[45]]
NULL
[[46]]
NULL
[[47]]
NULL
[[48]]
NULL
[[49]]
NULL
[[50]]
NULL
[[51]]
NULL
[[52]]
NULL
[[53]]
NULL
[[54]]
NULL
[[55]]
NULL
[[56]]
NULL
[[57]]
NULL
[[58]]
NULL
[[59]]
NULL
[[60]]
NULL
[[61]]
NULL
[[62]]
NULL
[[63]]
NULL
[[64]]
NULL
[[65]]
NULL
[[66]]
NULL
[[67]]
NULL
[[68]]
NULL
[[69]]
NULL
[[70]]
NULL
[[71]]
NULL
[[72]]
NULL
[[73]]
NULL
[[74]]
NULL
[[75]]
NULL
[[76]]
NULL
[[77]]
NULL
[[78]]
NULL
[[79]]
NULL
[[80]]
NULL
[[81]]
NULL
[[82]]
NULL
[[83]]
NULL
[[84]]
NULL
[[85]]
NULL
[[86]]
NULL
[[87]]
NULL
[[88]]
NULL
[[89]]
NULL
[[90]]
NULL
[[91]]
NULL
[[92]]
NULL
[[93]]
NULL
[[94]]
NULL
[[95]]
NULL
[[96]]
NULL
[[97]]
NULL
[[98]]
NULL
[[99]]
NULL
[[100]]
NULL
[[101]]
NULL
[[102]]
NULL
[[103]]
NULL
[[104]]
NULL
[[105]]
NULL
[[106]]
NULL
[[107]]
NULL
[[108]]
NULL
[[109]]
NULL
[[110]]
NULL
[[111]]
NULL
[[112]]
NULL
[[113]]
NULL
[[114]]
NULL
[[115]]
NULL
[[116]]
NULL
[[117]]
NULL
[[118]]
NULL
[[119]]
NULL
[[120]]
NULL
[[121]]
NULL
[[122]]
NULL
[[123]]
NULL
[[124]]
NULL
[[125]]
NULL
[[126]]
NULL
[[127]]
NULL
[[128]]
NULL
[[129]]
NULL
[[130]]
NULL
[[131]]
NULL
[[132]]
NULL
[[133]]
NULL
[[134]]
NULL
[[135]]
NULL
[[136]]
NULL
[[137]]
NULL
[[138]]
NULL
[[139]]
NULL
[[140]]
NULL
[[141]]
NULL
[[142]]
NULL
[[143]]
NULL
[[144]]
NULL
[[145]]
NULL
[[146]]
NULL
[[147]]
NULL
[[148]]
NULL
[[149]]
NULL
[[150]]
NULL
[[151]]
NULL
[[152]]
NULL
[[153]]
NULL
[[154]]
NULL
[[155]]
NULL
[[156]]
NULL
[[157]]
NULL
[[158]]
NULL
[[159]]
NULL
[[160]]
NULL
[[161]]
NULL
[[162]]
NULL
[[163]]
NULL
[[164]]
NULL
[[165]]
NULL
[[166]]
NULL
[[167]]
NULL
[[168]]
NULL
[[169]]
NULL
[[170]]
NULL
[[171]]
NULL
[[172]]
NULL
[[173]]
NULL
[[174]]
NULL
[[175]]
NULL
[[176]]
NULL
[[177]]
NULL
[[178]]
NULL
[[179]]
NULL
[[180]]
NULL
[[181]]
NULL
[[182]]
NULL
[[183]]
NULL
[[184]]
NULL
[[185]]
NULL
[[186]]
NULL
[[187]]
NULL
[[188]]
NULL
[[189]]
NULL
[[190]]
NULL
[[191]]
NULL
[[192]]
NULL
[[193]]
NULL
[[194]]
NULL
[[195]]
NULL
[[196]]
NULL
[[197]]
NULL
[[198]]
NULL
[[199]]
NULL
[[200]]
NULL
[[201]]
NULL
[[202]]
NULL
[[203]]
NULL
[[204]]
NULL
[[205]]
NULL
[[206]]
NULL
[[207]]
NULL
[[208]]
NULL
[[209]]
NULL
[[210]]
NULL
[[211]]
NULL
[[212]]
NULL
[[213]]
NULL
[[214]]
NULL
[[215]]
NULL
[[216]]
NULL
[[217]]
NULL
[[218]]
NULL
[[219]]
NULL
[[220]]
NULL
[[221]]
NULL
[[222]]
NULL
[[223]]
NULL
[[224]]
NULL
[[225]]
NULL
[[226]]
NULL
[[227]]
NULL
[[228]]
NULL
[[229]]
NULL
[[230]]
NULL
[[231]]
NULL
[[232]]
NULL
[[233]]
NULL
[[234]]
NULL
[[235]]
NULL
[[236]]
NULL
[[237]]
NULL
[[238]]
NULL
[[239]]
NULL
[[240]]
NULL
[[241]]
NULL
[[242]]
NULL
[[243]]
NULL
[[244]]
NULL
[[245]]
NULL
[[246]]
NULL
[[247]]
NULL
[[248]]
NULL
[[249]]
NULL
[[250]]
NULL
[[251]]
NULL
[[252]]
NULL
[[253]]
NULL
[[254]]
NULL
[[255]]
NULL
[[256]]
NULL
[[257]]
NULL
[[258]]
NULL
[[259]]
NULL
[[260]]
NULL
[[261]]
NULL
[[262]]
NULL
[[263]]
NULL
[[264]]
NULL
[[265]]
NULL
[[266]]
NULL
[[267]]
NULL
[[268]]
NULL
[[269]]
NULL
[[270]]
NULL
[[271]]
NULL
[[272]]
NULL
[[273]]
NULL
[[274]]
NULL
[[275]]
NULL
[[276]]
NULL
[[277]]
NULL
[[278]]
NULL
[[279]]
NULL
[[280]]
NULL
[[281]]
NULL
[[282]]
NULL
[[283]]
NULL
[[284]]
NULL
[[285]]
NULL
[[286]]
NULL
[[287]]
NULL
[[288]]
NULL
[[289]]
NULL
[[290]]
NULL
[[291]]
NULL
[[292]]
NULL
[[293]]
NULL
[[294]]
NULL
[[295]]
NULL
[[296]]
NULL
[[297]]
NULL
[[298]]
NULL
[[299]]
NULL
[[300]]
NULL
[[301]]
NULL
[[302]]
NULL
[[303]]
NULL
[[304]]
NULL
[[305]]
NULL
[[306]]
NULL
[[307]]
NULL
[[308]]
NULL
[[309]]
NULL
[[310]]
NULL
[[311]]
NULL
[[312]]
NULL
[[313]]
NULL
[[314]]
NULL
[[315]]
NULL
[[316]]
NULL
[[317]]
NULL
[[318]]
NULL
[[319]]
NULL
[[320]]
NULL
[[321]]
NULL
[[322]]
NULL
[[323]]
NULL
[[324]]
NULL
[[325]]
NULL
[[326]]
NULL
[[327]]
NULL
[[328]]
NULL
[[329]]
NULL
[[330]]
NULL
[[331]]
NULL
[[332]]
NULL
[[333]]
NULL
[[334]]
NULL
[[335]]
NULL
[[336]]
NULL
[[337]]
NULL
[[338]]
NULL
[[339]]
NULL
[[340]]
NULL
[[341]]
NULL
[[342]]
NULL
[[343]]
NULL
[[344]]
NULL
[[345]]
NULL
[[346]]
NULL
[[347]]
NULL
[[348]]
NULL
[[349]]
NULL
[[350]]
NULL
[[351]]
NULL
[[352]]
NULL
[[353]]
NULL
[[354]]
NULL
[[355]]
NULL
[[356]]
NULL
[[357]]
NULL
[[358]]
NULL
[[359]]
NULL
[[360]]
NULL
[[361]]
NULL
[[362]]
NULL
[[363]]
NULL
[[364]]
NULL
[[365]]
NULL
[[366]]
NULL
[[367]]
NULL
[[368]]
NULL
[[369]]
NULL
[[370]]
NULL
[[371]]
NULL
[[372]]
NULL
[[373]]
NULL
[[374]]
NULL
[[375]]
NULL
[[376]]
NULL
[[377]]
NULL
[[378]]
NULL
[[379]]
NULL
[[380]]
NULL
[[381]]
NULL
[[382]]
NULL
[[383]]
NULL
[[384]]
NULL
[[385]]
NULL
[[386]]
NULL
[[387]]
NULL
[[388]]
NULL
[[389]]
NULL
[[390]]
NULL
[[391]]
NULL
[[392]]
NULL
[[393]]
NULL
[[394]]
NULL
[[395]]
NULL
[[396]]
NULL
[[397]]
NULL
[[398]]
NULL
[[399]]
NULL
[[400]]
NULL
[[401]]
NULL
[[402]]
NULL
[[403]]
NULL
[[404]]
NULL
[[405]]
NULL
[[406]]
NULL
[[407]]
NULL
[[408]]
NULL
[[409]]
NULL
[[410]]
NULL
[[411]]
NULL
[[412]]
NULL
[[413]]
NULL
[[414]]
NULL
[[415]]
NULL
[[416]]
NULL
[[417]]
NULL
[[418]]
NULL
[[419]]
NULL
[[420]]
NULL
[[421]]
NULL
[[422]]
NULL
[[423]]
NULL
[[424]]
NULL
[[425]]
NULL
[[426]]
NULL
[[427]]
NULL
[[428]]
NULL
[[429]]
NULL
[[430]]
NULL
[[431]]
NULL
[[432]]
NULL
[[433]]
NULL
[[434]]
NULL
[[435]]
NULL
[[436]]
NULL
[[437]]
NULL
[[438]]
NULL
[[439]]
NULL
[[440]]
NULL
[[441]]
NULL
[[442]]
NULL
[[443]]
NULL
[[444]]
NULL
[[445]]
NULL
[[446]]
NULL
[[447]]
NULL
[[448]]
NULL
[[449]]
NULL
[[450]]
NULL
[[451]]
NULL
[[452]]
NULL
[[453]]
NULL
[[454]]
NULL
[[455]]
NULL
[[456]]
NULL
[[457]]
NULL
[[458]]
NULL
[[459]]
NULL
[[460]]
NULL
[[461]]
NULL
[[462]]
NULL
[[463]]
NULL
[[464]]
NULL
[[465]]
NULL
[[466]]
NULL
[[467]]
NULL
[[468]]
NULL
[[469]]
NULL
[[470]]
NULL
[[471]]
NULL
[[472]]
NULL
[[473]]
NULL
[[474]]
NULL
[[475]]
NULL
[[476]]
NULL
[[477]]
NULL
[[478]]
NULL
[[479]]
NULL
[[480]]
NULL
[[481]]
NULL
[[482]]
NULL
[[483]]
NULL
[[484]]
NULL
[[485]]
NULL
[[486]]
NULL
[[487]]
NULL
[[488]]
NULL
[[489]]
NULL
[[490]]
NULL
[[491]]
NULL
[[492]]
NULL
[[493]]
NULL
[[494]]
NULL
[[495]]
NULL
[[496]]
NULL
[[497]]
NULL
[[498]]
NULL
[[499]]
NULL
[[500]]
NULL
[[501]]
NULL
[[502]]
NULL
[[503]]
NULL
[[504]]
NULL
[[505]]
NULL
[[506]]
NULL
[[507]]
NULL
[[508]]
NULL
[[509]]
NULL
[[510]]
NULL
[[511]]
NULL
[[512]]
NULL
[[513]]
NULL
[[514]]
NULL
[[515]]
NULL
[[516]]
NULL
[[517]]
NULL
[[518]]
NULL
[[519]]
NULL
[[520]]
NULL
[[521]]
NULL
[[522]]
NULL
[[523]]
NULL
[[524]]
NULL
[[525]]
NULL
[[526]]
NULL
[[527]]
NULL
[[528]]
NULL
[[529]]
NULL
[[530]]
NULL
[[531]]
NULL
[[532]]
NULL
[[533]]
NULL
[[534]]
NULL
[[535]]
NULL
[[536]]
NULL
[[537]]
NULL
[[538]]
NULL
[[539]]
NULL
[[540]]
NULL
[[541]]
NULL
[[542]]
NULL
[[543]]
NULL
[[544]]
NULL
[[545]]
NULL
[[546]]
NULL
[[547]]
NULL
[[548]]
NULL
[[549]]
NULL
[[550]]
NULL
[[551]]
NULL
[[552]]
NULL
[[553]]
NULL
[[554]]
NULL
[[555]]
NULL
[[556]]
NULL
[[557]]
NULL
[[558]]
NULL
[[559]]
NULL
[[560]]
NULL
[[561]]
NULL
[[562]]
NULL
[[563]]
NULL
[[564]]
NULL
[[565]]
NULL
[[566]]
NULL
[[567]]
NULL
[[568]]
NULL
[[569]]
NULL
[[570]]
NULL
[[571]]
NULL
[[572]]
NULL
[[573]]
NULL
[[574]]
NULL
[[575]]
NULL
[[576]]
NULL
[[577]]
NULL
[[578]]
NULL
[[579]]
NULL
[[580]]
NULL
[[581]]
NULL
[[582]]
NULL
[[583]]
NULL
[[584]]
NULL
[[585]]
NULL
[[586]]
NULL
[[587]]
NULL
[[588]]
NULL
[[589]]
NULL
[[590]]
NULL
[[591]]
NULL
[[592]]
NULL
[[593]]
NULL
[[594]]
NULL
[[595]]
NULL
[[596]]
NULL
[[597]]
NULL
[[598]]
NULL
[[599]]
NULL
[[600]]
NULL
[[601]]
NULL
[[602]]
NULL
[[603]]
NULL
[[604]]
NULL
[[605]]
NULL
[[606]]
NULL
[[607]]
NULL
[[608]]
NULL
[[609]]
NULL
[[610]]
NULL
[[611]]
NULL
[[612]]
NULL
[[613]]
NULL
[[614]]
NULL
[[615]]
NULL
[[616]]
NULL
[[617]]
NULL
[[618]]
NULL
[[619]]
NULL
[[620]]
NULL
[[621]]
NULL
[[622]]
NULL
[[623]]
NULL
[[624]]
NULL
[[625]]
NULL
[[626]]
NULL
[[627]]
NULL
[[628]]
NULL
[[629]]
NULL
[[630]]
NULL
[[631]]
NULL
[[632]]
NULL
[[633]]
NULL
[[634]]
NULL
[[635]]
NULL
[[636]]
NULL
[[637]]
NULL
[[638]]
NULL
[[639]]
NULL
[[640]]
NULL
[[641]]
NULL
[[642]]
NULL
[[643]]
NULL
[[644]]
NULL
[[645]]
NULL
[[646]]
NULL
[[647]]
NULL
[[648]]
NULL
[[649]]
NULL
[[650]]
NULL
[[651]]
NULL
[[652]]
NULL
[[653]]
NULL
[[654]]
NULL
[[655]]
NULL
[[656]]
NULL
[[657]]
NULL
[[658]]
NULL
[[659]]
NULL
[[660]]
NULL
[[661]]
NULL
[[662]]
NULL
[[663]]
NULL
[[664]]
NULL
[[665]]
NULL
[[666]]
NULL
[[667]]
NULL
[[668]]
NULL
[[669]]
NULL
[[670]]
NULL
[[671]]
NULL
[[672]]
NULL
[[673]]
NULL
[[674]]
NULL
[[675]]
NULL
[[676]]
NULL
[[677]]
NULL
[[678]]
NULL
[[679]]
NULL
[[680]]
NULL
[[681]]
NULL
[[682]]
NULL
[[683]]
NULL
[[684]]
NULL
[[685]]
NULL
[[686]]
NULL
[[687]]
NULL
[[688]]
NULL
[[689]]
NULL
[[690]]
NULL
[[691]]
NULL
[[692]]
NULL
[[693]]
NULL
[[694]]
NULL
[[695]]
NULL
[[696]]
NULL
[[697]]
NULL
[[698]]
NULL
[[699]]
NULL
[[700]]
NULL
[[701]]
NULL
[[702]]
NULL
[[703]]
NULL
[[704]]
NULL
[[705]]
NULL
[[706]]
NULL
[[707]]
NULL
[[708]]
NULL
[[709]]
NULL
[[710]]
NULL
[[711]]
NULL
[[712]]
NULL
[[713]]
NULL
[[714]]
NULL
[[715]]
NULL
[[716]]
NULL
[[717]]
NULL
[[718]]
NULL
[[719]]
NULL
[[720]]
NULL
[[721]]
NULL
[[722]]
NULL
[[723]]
NULL
[[724]]
NULL
[[725]]
NULL
[[726]]
NULL
[[727]]
NULL
[[728]]
NULL
[[729]]
NULL
[[730]]
NULL
[[731]]
NULL
[[732]]
NULL
[[733]]
NULL
[[734]]
NULL
[[735]]
NULL
[[736]]
NULL
[[737]]
NULL
[[738]]
NULL
[[739]]
NULL
[[740]]
NULL
[[741]]
NULL
[[742]]
NULL
[[743]]
NULL
[[744]]
NULL
[[745]]
NULL
[[746]]
NULL
[[747]]
NULL
[[748]]
NULL
[[749]]
NULL
[[750]]
NULL
[[751]]
NULL
[[752]]
NULL
[[753]]
NULL
[[754]]
NULL
[[755]]
NULL
[[756]]
NULL
[[757]]
NULL
[[758]]
NULL
[[759]]
NULL
[[760]]
NULL
[[761]]
NULL
[[762]]
NULL
[[763]]
NULL
[[764]]
NULL
[[765]]
NULL
[[766]]
NULL
[[767]]
NULL
[[768]]
NULL
[[769]]
NULL
[[770]]
NULL
[[771]]
NULL
[[772]]
NULL
[[773]]
NULL
[[774]]
NULL
[[775]]
NULL
[[776]]
NULL
[[777]]
NULL
[[778]]
NULL
[[779]]
NULL
[[780]]
NULL
[[781]]
NULL
[[782]]
NULL
[[783]]
NULL
[[784]]
NULL
[[785]]
NULL
[[786]]
NULL
[[787]]
NULL
[[788]]
NULL
[[789]]
NULL
[[790]]
NULL
[[791]]
NULL
[[792]]
NULL
[[793]]
NULL
[[794]]
NULL
[[795]]
NULL
[[796]]
NULL
[[797]]
NULL
[[798]]
NULL
[[799]]
NULL
[[800]]
NULL
[[801]]
NULL
[[802]]
NULL
[[803]]
NULL
[[804]]
NULL
[[805]]
NULL
[[806]]
NULL
[[807]]
NULL
[[808]]
NULL
[[809]]
NULL
[[810]]
NULL
[[811]]
NULL
[[812]]
NULL
[[813]]
NULL
[[814]]
NULL
[[815]]
NULL
[[816]]
NULL
[[817]]
NULL
[[818]]
NULL
[[819]]
NULL
[[820]]
NULL
[[821]]
NULL
[[822]]
NULL
[[823]]
NULL
[[824]]
NULL
[[825]]
NULL
[[826]]
NULL
[[827]]
NULL
[[828]]
NULL
[[829]]
NULL
[[830]]
NULL
[[831]]
NULL
[[832]]
NULL
[[833]]
NULL
[[834]]
NULL
[[835]]
NULL
[[836]]
NULL
[[837]]
NULL
[[838]]
NULL
[[839]]
NULL
[[840]]
NULL
[[841]]
NULL
[[842]]
NULL
[[843]]
NULL
[[844]]
NULL
[[845]]
NULL
[[846]]
NULL
[[847]]
NULL
[[848]]
NULL
[[849]]
NULL
[[850]]
NULL
[[851]]
NULL
[[852]]
NULL
[[853]]
NULL
[[854]]
NULL
[[855]]
NULL
[[856]]
NULL
[[857]]
NULL
[[858]]
NULL
[[859]]
NULL
[[860]]
NULL
[[861]]
NULL
[[862]]
NULL
[[863]]
NULL
[[864]]
NULL
[[865]]
NULL
[[866]]
NULL
[[867]]
NULL
[[868]]
NULL
[[869]]
NULL
[[870]]
NULL
[[871]]
NULL
[[872]]
NULL
[[873]]
NULL
[[874]]
NULL
[[875]]
NULL
[[876]]
NULL
[[877]]
NULL
[[878]]
NULL
[[879]]
NULL
[[880]]
NULL
[[881]]
NULL
[[882]]
NULL
[[883]]
NULL
[[884]]
NULL
[[885]]
NULL
[[886]]
NULL
[[887]]
NULL
[[888]]
NULL
[[889]]
NULL
[[890]]
NULL
[[891]]
NULL
[[892]]
NULL
[[893]]
NULL
[[894]]
NULL
[[895]]
NULL
[[896]]
NULL
[[897]]
NULL
[[898]]
NULL
[[899]]
NULL
[[900]]
NULL
[[901]]
NULL
[[902]]
NULL
[[903]]
NULL
[[904]]
NULL
[[905]]
NULL
[[906]]
NULL
[[907]]
NULL
[[908]]
NULL
[[909]]
NULL
[[910]]
NULL
[[911]]
NULL
[[912]]
NULL
[[913]]
NULL
[[914]]
NULL
[[915]]
NULL
[[916]]
NULL
[[917]]
NULL
[[918]]
NULL
[[919]]
NULL
[[920]]
NULL
[[921]]
NULL
[[922]]
NULL
[[923]]
NULL
[[924]]
NULL
[[925]]
NULL
[[926]]
NULL
[[927]]
NULL
[[928]]
NULL
[[929]]
NULL
[[930]]
NULL
[[931]]
NULL
[[932]]
NULL
[[933]]
NULL
[[934]]
NULL
[[935]]
NULL
[[936]]
NULL
[[937]]
NULL
[[938]]
NULL
[[939]]
NULL
[[940]]
NULL
[[941]]
NULL
[[942]]
NULL
[[943]]
NULL
[[944]]
NULL
[[945]]
NULL
[[946]]
NULL
[[947]]
NULL
[[948]]
NULL
[[949]]
NULL
[[950]]
NULL
[[951]]
NULL
[[952]]
NULL
[[953]]
NULL
[[954]]
NULL
[[955]]
NULL
[[956]]
NULL
[[957]]
NULL
[[958]]
NULL
[[959]]
NULL
[[960]]
NULL
[[961]]
NULL
[[962]]
NULL
[[963]]
NULL
[[964]]
NULL
[[965]]
NULL
[[966]]
NULL
[[967]]
NULL
[[968]]
NULL
[[969]]
NULL
[[970]]
NULL
[[971]]
NULL
[[972]]
NULL
[[973]]
NULL
[[974]]
NULL
[[975]]
NULL
[[976]]
NULL
[[977]]
NULL
[[978]]
NULL
[[979]]
NULL
[[980]]
NULL
[[981]]
NULL
[[982]]
NULL
[[983]]
NULL
[[984]]
NULL
[[985]]
NULL
[[986]]
NULL
[[987]]
NULL
[[988]]
NULL
[[989]]
NULL
[[990]]
NULL
[[991]]
NULL
[[992]]
NULL
[[993]]
NULL
[[994]]
NULL
[[995]]
NULL
[[996]]
NULL
[[997]]
NULL
[[998]]
NULL
[[999]]
NULL
[[1000]]
NULL
[[1001]]
NULL
plot(chain.step$lnL, type="n", ylim=c(-700, -500))
lines(1:length(chain.sigmoid$lnL), chain.sigmoid$lnL, col="green")lines(1:length(chain.linear$lnL), chain.linear$lnL, col="blue")
lines(1:length(chain.step$lnL), chain.step$lnL, col="red")
saveRDS(chain.sigmoid, "../output/OUwie/chain.sigmoid")
saveRDS(chain.linear, "../output/OUwie/chain.linear")
saveRDS(chain.step, "../output/OUwie/chain.step")Let’s do a likelihood based analysis.
library(optimx)
i <- 10
sb <- strees.bayou[[i]]$sb
t2 <- strees.bayou[[i]]$t2
loc <- strees.bayou[[i]]$loc
k <- length(sb)
ntheta <- ncat - 1
lik.sigmoid <- function(pp){
pars <- list(alpha = pp[1],
sig2 = pp[2],
beta_left = pp[3],
beta_right = pp[4],
beta_center = pp[5],
beta_slope = pp[6],
k = k,
ntheta=ntheta,
theta = pp[3] + pp[4]/(1 + exp(pp[6] * (midbins - pp[5]))),
sb = sb,
loc=loc,
t2=t2)
model.sigmoidTheta$lik.fn(pars, cache, cache$dat)$loglik[,1]
}
## Parameter order: alpha, sig2, beta_left, beta_right, beta_center, beta_slope
pars.sigmoid <- pull.pars(length(chain.sigmoid$gen), chain.sigmoid, model.sigmoidTheta)
pp.sigmoid <- unname(c(pars.sigmoid$alpha, pars.sigmoid$sig2, pars.sigmoid$beta_left, pars.sigmoid$beta_right, pars.sigmoid$beta_center, pars.sigmoid$beta_slope))
lik.step <- function(pp){
pars <- list(alpha = pp[1],
sig2 = pp[2],
beta_left = pp[3],
beta_right = pp[4],
beta_center = pp[5],
k = k,
ntheta=ntheta,
theta = ifelse(midbins>pp[5], pp[4], pp[3]),
sb = sb,
loc=loc,
t2=t2)
model.stepTheta$lik.fn(pars, cache, cache$dat)$loglik[,1]
}
## Parameter order: alpha, sig2, beta_left, beta_right, beta_center, beta_slope
pars.step <- pull.pars(length(chain.step$gen), chain.step, model.stepTheta)
pp.step <- unname(c(pars.step$alpha, pars.step$sig2, pars.step$beta_left, pars.step$beta_right, pars.step$beta_center))
lik.linear <- function(pp){
pars <- list(alpha = pp[1],
sig2 = pp[2],
beta_intercept = pp[3],
beta_slope = pp[4],
k = k,
ntheta=ntheta,
theta = pp[3] + pp[4]*midbins,
sb = sb,
loc=loc,
t2=t2)
model.linearTheta$lik.fn(pars, cache, cache$dat)$loglik[,1]
}
## Parameter order: alpha, sig2, beta_left, beta_right, beta_center, beta_slope
pars.linear <- pull.pars(length(chain.linear$gen), chain.linear, model.linearTheta)
pp.linear <- unname(c(pars.linear$alpha, pars.linear$sig2, pars.linear$beta_intercept, pars.linear$beta_slope))opt.sigmoid <- optimx(pp.sigmoid, lik.sigmoid, control=list(maximize=TRUE), method=c('nlminb'), lower=c(0,0,-12,-12,-200, -0.1), upper=c(1000, 1000, 0, 0, 200, 10))
opt.step <- optimx(pp.step, lik.step, control=list(maximize=TRUE), method=c('nlminb'), lower=c(0,0,-12,-12,-200),
upper=c(1000, 1000, 0, 0, 200))
opt.linear <- optimx(pp.linear, lik.linear, control=list(maximize=TRUE), method=c('nlminb'), lower=c(0,0,-12,-1),
upper=c(1000, 1000, 0, 1))
aics <- list()
aics[['aic.sigmoid']] <- 2*6 - 2*opt.sigmoid$value
aics[['aic.step']] <- 2*5 - 2*opt.step$value
aics[['aic.linear']] <- 2*4 - 2*opt.linear$value
aicsplot(midbins, startpar.linear$theta, ylim=c(-10, 0), type="n")
x <- -350:200
points(td[['tmin.025']], td[['lnVs']], pch=21, bg="gray50")
Vy.linear <- sqrt(opt.linear$p2/(2*opt.linear$p1))
lines(x, opt.linear$p3+opt.linear$p4*x, col=makeTransparent(rgb(0, 0, 1), alpha=255), lwd=2)
lines(x, (opt.linear$p3+opt.linear$p4*x) + 2*Vy.linear, col=makeTransparent(rgb(0, 0, 1), alpha=255), lwd=2, lty=2)
lines(x, (opt.linear$p3+opt.linear$p4*x) - 2*Vy.linear, col=makeTransparent(rgb(0, 0, 1), alpha=255), lwd=2, lty=2)
Vy.sigmoid <- opt.sigmoid$p2/(2*opt.sigmoid$p1)
lines(x, opt.sigmoid$p3+opt.sigmoid$p4/(1 + exp(opt.sigmoid$p6*(x-opt.sigmoid$p5))), col=makeTransparent(rgb(0, 1, 0), alpha=255), lwd=2)
lines(x, (opt.sigmoid$p3+opt.sigmoid$p4/(1 + exp(opt.sigmoid$p6*(x-opt.sigmoid$p5)))) + 2*Vy.sigmoid, col=makeTransparent(rgb(0, 1, 0), alpha=255), lwd=2, lty=2)
lines(x, (opt.sigmoid$p3+opt.sigmoid$p4/(1 + exp(opt.sigmoid$p6*(x-opt.sigmoid$p5)))) - 2*Vy.sigmoid, col=makeTransparent(rgb(0, 1, 0), alpha=255), lwd=2, lty=2)
Vy.step <- opt.step$p2/(2*opt.step$p1)
lines(x, ifelse(x > opt.step$p5, opt.step$p4, opt.step$p3), col=makeTransparent(rgb(1, 0, 0), alpha=255), lwd=2)
lines(x, ifelse(x > opt.step$p5, opt.step$p4, opt.step$p3) + 2*Vy.step, col=makeTransparent(rgb(1, 0, 0), alpha=255), lwd=2, lty=2)
lines(x, ifelse(x > opt.step$p5, opt.step$p4, opt.step$p3) - 2*Vy.step, col=makeTransparent(rgb(1, 0, 0), alpha=255), lwd=2, lty=2)
## Script it over all trees.
optimizeModelsOverSimmap <- function(i){
sb <- strees.bayou[[i]]$sb
t2 <- strees.bayou[[i]]$t2
loc <- strees.bayou[[i]]$loc
k <- length(sb)
ntheta <- ncat - 1
lik.sigmoid <- function(pp){
pars <- list(alpha = pp[1],
sig2 = pp[2],
beta_left = pp[3],
beta_right = pp[4],
beta_center = pp[5],
beta_slope = pp[6],
k = k,
ntheta=ntheta,
theta = pp[3] + pp[4]/(1 + exp(pp[6] * (midbins - pp[5]))),
sb = sb,
loc=loc,
t2=t2)
model.sigmoidTheta$lik.fn(pars, cache, cache$dat)$loglik[,1]
}
lik.sigmoid0 <- function(pp){
pars <- list(alpha = pp[1],
sig2 = pp[2],
beta_left = pp[3],
beta_right = pp[4],
beta_center = 0,
beta_slope = pp[5],
k = k,
ntheta=ntheta,
theta = pp[3] + pp[4]/(1 + exp(pp[5] * (midbins - 0))),
sb = sb,
loc=loc,
t2=t2)
model.sigmoidTheta$lik.fn(pars, cache, cache$dat)$loglik[,1]
}
lik.tanhLin <- function(pp){
pars <- list(alpha = pp[1],
sig2 = pp[2],
beta_left = pp[3],
beta_right = pp[4],
beta_center = pp[5],
beta_slope = pp[6],
k = k,
ntheta=ntheta,
theta = pp[3] + pp[6]*midbins + pp[4]*tanh(midbins - pp[5]),
sb = sb,
loc=loc,
t2=t2)
model.sigmoidTheta$lik.fn(pars, cache, cache$dat)$loglik[,1]
}
## Parameter order: alpha, sig2, beta_left, beta_right, beta_center, beta_slope
pars.sigmoid <- pull.pars(length(chain.sigmoid$gen), chain.sigmoid, model.sigmoidTheta)
pp.sigmoid <- unname(c(pars.sigmoid$alpha, pars.sigmoid$sig2, pars.sigmoid$beta_left, pars.sigmoid$beta_right, pars.sigmoid$beta_center, pars.sigmoid$beta_slope))
pp.sigmoid0 <- pp.sigmoid[-5]
lik.step <- function(pp){
pars <- list(alpha = pp[1],
sig2 = pp[2],
beta_left = pp[3],
beta_right = pp[4],
beta_center = pp[5],
k = k,
ntheta=ntheta,
theta = ifelse(midbins>pp[5], pp[4], pp[3]),
sb = sb,
loc=loc,
t2=t2)
model.stepTheta$lik.fn(pars, cache, cache$dat)$loglik[,1]
}
lik.step0 <- function(pp){
pars <- list(alpha = pp[1],
sig2 = pp[2],
beta_left = pp[3],
beta_right = pp[4],
beta_center = 0,
k = k,
ntheta=ntheta,
theta = ifelse(midbins>0, pp[4], pp[3]),
sb = sb,
loc=loc,
t2=t2)
model.stepTheta$lik.fn(pars, cache, cache$dat)$loglik[,1]
}
## Parameter order: alpha, sig2, beta_left, beta_right, beta_center, beta_slope
pars.step <- pull.pars(length(chain.step$gen), chain.step, model.stepTheta)
pp.step <- unname(c(pars.step$alpha, pars.step$sig2, pars.step$beta_left, pars.step$beta_right, pars.step$beta_center))
pp.step0 <- pp.step[-5]
lik.linear <- function(pp){
pars <- list(alpha = pp[1],
sig2 = pp[2],
beta_intercept = pp[3],
beta_slope = pp[4],
k = k,
ntheta=ntheta,
theta = pp[3] + pp[4]*midbins,
sb = sb,
loc=loc,
t2=t2)
model.linearTheta$lik.fn(pars, cache, cache$dat)$loglik[,1]
}
## Parameter order: alpha, sig2, beta_left, beta_right, beta_center, beta_slope
pars.linear <- pull.pars(length(chain.linear$gen), chain.linear, model.linearTheta)
pp.linear <- unname(c(pars.linear$alpha, pars.linear$sig2, pars.linear$beta_intercept, pars.linear$beta_slope))
pp.tanhLin <- unname(c(pars.linear$alpha, pars.linear$sig2, pars.linear$beta_intercept, rnorm(1), runif(1, -10, 10), pars.linear$beta_slope))
opt.sigmoid <- optimx(pp.sigmoid, lik.sigmoid, control=list(maximize=TRUE), method=c('nlminb'), lower=c(0,0,-12,-12,-200, -0.1),
upper=c(2000, 2000, 0, 0, 200, 10))
opt.step <- optimx(pp.step, lik.step, control=list(maximize=TRUE), method=c('nlminb'), lower=c(0,0,-12,-12,-200),
upper=c(2000, 2000, 0, 0, 200))
opt.sigmoid0 <- optimx(pp.sigmoid0, lik.sigmoid0, control=list(maximize=TRUE), method=c('nlminb'), lower=c(0,0,-12,-12,-0.1),
upper=c(2000, 2000, 0, 0, 10))
opt.step0 <- optimx(pp.step0, lik.step0, control=list(maximize=TRUE), method=c('nlminb'), lower=c(0,0,-12,-12),
upper=c(2000, 2000, 0, 0))
opt.linear <- optimx(pp.linear, lik.linear, control=list(maximize=TRUE), method=c('nlminb'), lower=c(0,0,-12,-1),
upper=c(2000, 2000, 0, 1))
opt.tanhLin <- optimx(pp.tanhLin, lik.tanhLin, control=list(maximize=TRUE), method=c('nlminb'), lower=c(0,0,-12, -5,-200, -1), upper=c(2000, 2000, 0, 5, 200, 1))
aics <- list()
aics[['aic.sigmoid']] <- 2*6 - 2*opt.sigmoid$value
aics[['aic.step']] <- 2*5 - 2*opt.step$value
aics[['aic.linear']] <- 2*4 - 2*opt.linear$value
aics[['aic.sigmoid0']] <- 2*5 - 2*opt.sigmoid0$value
aics[['aic.step0']] <- 2*4 - 2*opt.step0$value
aics[['aic.tanhLin']] <- 2*6 - 2*opt.tanhLin$value
aics
return(list(opt.sigmoid=opt.sigmoid, opt.linear=opt.linear, opt.step=opt.step, opt.sigmoid0=opt.sigmoid0, opt.step0=opt.step0, opt.tanhLin=opt.tanhLin, aics=aics))
}
opt.allmodels <- lapply(1:100, optimizeModelsOverSimmap)
all.sigmoid <- do.call(rbind, lapply(opt.allmodels, function(x) x$opt.sigmoid))
all.step <- do.call(rbind, lapply(opt.allmodels, function(x) x$opt.step))
all.sigmoid0 <- do.call(rbind, lapply(opt.allmodels, function(x) x$opt.sigmoid0))
all.step0 <- do.call(rbind, lapply(opt.allmodels, function(x) x$opt.step0))
all.linear <- do.call(rbind, lapply(opt.allmodels, function(x) x$opt.linear))
all.tanhLin <- do.call(rbind, lapply(opt.allmodels, function(x) x$opt.tanhLin))
all.aics <- do.call(rbind, lapply(opt.allmodels, function(x) do.call(cbind, x$aics)))
boxplot(all.aics)
all.aics <- as.data.frame(all.aics)
t.test(all.aics$aic.sigmoid, all.aics$aic.linear, paired = TRUE)
Paired t-test
data: all.aics$aic.sigmoid and all.aics$aic.linear
t = -3.9995, df = 99, p-value = 0.0001225
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-1.6718993 -0.5630916
sample estimates:
mean of the differences
-1.117495 tmp <- reshape2::melt(all.aics)No id variables; using all as measure variablescat("Akaike weights\n")Akaike weightsaicws <- aicw(tmp$value)$w
round(tapply(aicws, tmp$variable, sum),2) aic.sigmoid aic.step aic.linear aic.sigmoid0 aic.step0 aic.tanhLin
0.52 0.00 0.00 0.42 0.00 0.05 dAICs <- apply(all.aics, 1, function(x) x-min(x, na.rm=TRUE))
par(mar=c(10, 4, 1,1))
boxplot(t(dAICs), las=2, xlab="Model", ylab="dAIC")
boxplot(t(dAICs), las=2, xlab="Model", ylab="dAIC", ylim=c(0,8))
cat("Mean dAIC values\n")Mean dAIC valuesapply(t(dAICs), 2, mean) aic.sigmoid aic.step aic.linear aic.sigmoid0 aic.step0 aic.tanhLin
1.4509317 41.1330105 2.5684272 0.2571837 39.1330105 3.3319121 Independent analyses for Deciduous vs. Evergreen
Let’s filter by phenology and analyze each independently. This is not ideal, since transitions occur between deciduous and evergreen species, and are not accounted for when we filter this way. However, if phylogenetic half-lives are small, this is probably of negligible importance.
## Set up the bins:
ncat <- 10
pred <- "tmin.01"
trait <- "lnVs"
bins <- seq(min(td[[pred]])-0.1, max(td[[pred]])+0.1, length.out=ncat)
midbins <- (bins[2:length(bins)] + bins[1:(length(bins)-1)])/2
td <- mutate(td, binpred = as.numeric(cut(td[[pred]], breaks=bins, include.lowest=TRUE)))
hist(td[['binpred']])
bins [1] -395.10000 -329.11667 -263.13333 -197.15000 -131.16667 -65.18333 0.80000 66.78333 132.76667 198.75000td$phy$edge.length <- td$phy$edge.length/max(branching.times(td$phy))
tree <- td$phy
dat <- td$dattdEV <- filter(td, phenology == "EV")
tdD <- filter(td, phenology == "D")
tdEV$phy <- reorder(tdEV$phy, "postorder")
tdD$phy <- reorder(tdD$phy, "postorder")
#tdEV$phy$edge.length <- tdEV$phy$edge.length/max(branching.times(tdEV$phy))*100
## Use diversitree to fit a meristic model:
mknEV.lik <- make.mkn.meristic(tdEV$phy, tdEV[['binpred']], k = ncat)
mknD.lik <- make.mkn.meristic(tdD$phy, tdD[['binpred']], k = ncat)
mknEV.lik <- constrain(mknEV.lik, q.up~q.down)
mknD.lik <- constrain(mknD.lik, q.up~q.down)
p <- c(0.1)
EVfit.mkn <- find.mle(mknEV.lik, p)
Dfit.mkn <- find.mle(mknD.lik, p)
Q.EV <- matrix(0, ncol=ncat-1, nrow=ncat-1)
for(i in 1:(ncat-2)){
Q.EV[i, i+1] <- EVfit.mkn$par[1]
Q.EV[i+1, i] <- EVfit.mkn$par[1]
}
Q.D <- matrix(0, ncol=ncat-1, nrow=ncat-1)
for(i in 1:(ncat-2)){
Q.D[i, i+1] <- Dfit.mkn$par[1]
Q.D[i+1, i] <- Dfit.mkn$par[1]
}
diag(Q.EV) <- apply(Q.EV, 1, sum)*-1
diag(Q.D) <- apply(Q.D, 1, sum)*-1
rownames(Q.EV) <- colnames(Q.EV) <- 1:ncol(Q.EV)
rownames(Q.D) <- colnames(Q.D) <- 1:ncol(Q.D)
simmap2bayou <- function(stree){
shifts <- sapply(stree$maps, function(x) names(x)[-1])
sb <- unlist(sapply(1:length(shifts), function(x) rep(x, length(shifts[[x]]))))
t2 <- as.numeric(unlist(sapply(stree$maps, function(x) names(x)[-1])))
loc <- unlist(sapply(stree$maps, function(x) cumsum(x)[-1]))
return(list(shifts=shifts, sb=sb, t2=t2, loc=loc))
}
#fdfit <- fitDiscrete(td$phy, td[['binpred']], model="meristic")
#Qg <- geiger:::.Qmatrix.from.gfit(fdfit)
#stree <- reorderSimmap(make.simmap(td$phy, td[['binpred']], Q=Q), "postorder")
strees.EV <- make.simmap(tdEV$phy, tdEV[['binpred']], Q=Q.EV, nsim=100)make.simmap is sampling character histories conditioned on the transition matrix
Q =
1 2 3 4 5 6 7 8 9
1 -4.891797 4.891797 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
2 4.891797 -9.783594 4.891797 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
3 0.000000 4.891797 -9.783594 4.891797 0.000000 0.000000 0.000000 0.000000 0.000000
4 0.000000 0.000000 4.891797 -9.783594 4.891797 0.000000 0.000000 0.000000 0.000000
5 0.000000 0.000000 0.000000 4.891797 -9.783594 4.891797 0.000000 0.000000 0.000000
6 0.000000 0.000000 0.000000 0.000000 4.891797 -9.783594 4.891797 0.000000 0.000000
7 0.000000 0.000000 0.000000 0.000000 0.000000 4.891797 -9.783594 4.891797 0.000000
8 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 4.891797 -9.783594 4.891797
9 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 4.891797 -4.891797
(specified by the user);
and (mean) root node prior probabilities
pi =
1 2 3 4 5 6 7 8 9
0.1111111 0.1111111 0.1111111 0.1111111 0.1111111 0.1111111 0.1111111 0.1111111 0.1111111 Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Done.strees.D <- make.simmap(tdD$phy, tdD[['binpred']], Q=Q.D, nsim=100)make.simmap is sampling character histories conditioned on the transition matrix
Q =
1 2 3 4 5 6 7 8 9
1 -12.19336 12.19336 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000
2 12.19336 -24.38672 12.19336 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000
3 0.00000 12.19336 -24.38672 12.19336 0.00000 0.00000 0.00000 0.00000 0.00000
4 0.00000 0.00000 12.19336 -24.38672 12.19336 0.00000 0.00000 0.00000 0.00000
5 0.00000 0.00000 0.00000 12.19336 -24.38672 12.19336 0.00000 0.00000 0.00000
6 0.00000 0.00000 0.00000 0.00000 12.19336 -24.38672 12.19336 0.00000 0.00000
7 0.00000 0.00000 0.00000 0.00000 0.00000 12.19336 -24.38672 12.19336 0.00000
8 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 12.19336 -24.38672 12.19336
9 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 12.19336 -12.19336
(specified by the user);
and (mean) root node prior probabilities
pi =
1 2 3 4 5 6 7 8 9
0.1111111 0.1111111 0.1111111 0.1111111 0.1111111 0.1111111 0.1111111 0.1111111 0.1111111 Done.for(i in 1:length(strees.EV)) strees.EV[[i]] <- reorderSimmap(strees.EV[[i]], "postorder")
for(i in 1:length(strees.D)) strees.D[[i]] <- reorderSimmap(strees.D[[i]], "postorder")
EVtrees.bayou <- lapply(strees.EV, simmap2bayou)
Dtrees.bayou <- lapply(strees.D, simmap2bayou)saveRDS(EVtrees.bayou, file="../output/OUwie/EVtrees.rds")
saveRDS(Dtrees.bayou, file="../output/OUwie/Dtrees.rds")
EVtrees.bayou <- readRDS(file="../output/OUwie/EVtrees.rds")
Dtrees.bayou <- readRDS(file="../output/OUwie/Dtrees.rds")## Starting parameters for each function type:
## Step function
step.starts <- c(0, -8, -4)
startpar.stepEVD <- list(
EV = list(alpha=1, sig2=1,
beta_left=step.starts[2],
beta_right=step.starts[3],
beta_center=step.starts[1],
k=length(EVtrees.bayou[[1]]$sb), ntheta=ncat-1,
theta=ifelse(midbins > step.starts[1], step.starts[3], step.starts[2]),
sb=EVtrees.bayou[[1]]$sb, loc=EVtrees.bayou[[1]]$loc, t2=EVtrees.bayou[[1]]$t2),
D = list(alpha=1, sig2=1,
beta_left=step.starts[2],
beta_right=step.starts[3],
beta_center=step.starts[1],
k=length(Dtrees.bayou[[1]]$sb), ntheta=ncat-1,
theta=ifelse(midbins > step.starts[1], step.starts[3], step.starts[2]),
sb=Dtrees.bayou[[1]]$sb, loc=Dtrees.bayou[[1]]$loc, t2=Dtrees.bayou[[1]]$t2)
)
plot(midbins, startpar.stepEVD$EV$theta)
sigmoid.starts <- list(beta_left = -2, beta_right = -6, beta_center = 0, beta_slope = 0.02)
startpar.sigmoidEVD <- list(
EV = list(alpha=1, sig2=0.5,
beta_left=sigmoid.starts$beta_left,
beta_right=sigmoid.starts$beta_right,
beta_center=sigmoid.starts$beta_center,
beta_slope = sigmoid.starts$beta_slope,
k=length(EVtrees.bayou[[1]]$sb), ntheta=ncat-1,
theta= sigmoid.starts$beta_left + sigmoid.starts$beta_right/(1 + exp(sigmoid.starts$beta_slope * (midbins - sigmoid.starts$beta_center))),
sb=EVtrees.bayou[[1]]$sb, loc=EVtrees.bayou[[1]]$loc, t2=EVtrees.bayou[[1]]$t2),
D = list(alpha=1, sig2=0.5,
beta_left=sigmoid.starts$beta_left,
beta_right=sigmoid.starts$beta_right,
beta_center=sigmoid.starts$beta_center,
beta_slope = sigmoid.starts$beta_slope,
k=length(Dtrees.bayou[[1]]$sb), ntheta=ncat-1,
theta= sigmoid.starts$beta_left + sigmoid.starts$beta_right/(1 + exp(sigmoid.starts$beta_slope * (midbins - sigmoid.starts$beta_center))),
sb=Dtrees.bayou[[1]]$sb, loc=Dtrees.bayou[[1]]$loc, t2=Dtrees.bayou[[1]]$t2)
)
plot(midbins, startpar.sigmoidEVD$EV$theta)
linear.starts <- list(beta_intercept = -5, beta_slope = 0.01)
startpar.linearEVD <- list(
EV = list(alpha=1, sig2=0.5,
beta_intercept=linear.starts$beta_intercept,
beta_slope=linear.starts$beta_slope,
k=length(EVtrees.bayou[[1]]$sb), ntheta=ncat-1,
theta= linear.starts$beta_intercept + linear.starts$beta_slope*midbins,
sb=EVtrees.bayou[[1]]$sb, loc=EVtrees.bayou[[1]]$loc, t2=EVtrees.bayou[[1]]$t2),
D = list(alpha=1, sig2=0.5,
beta_intercept=linear.starts$beta_intercept,
beta_slope=linear.starts$beta_slope,
k=length(EVtrees.bayou[[1]]$sb), ntheta=ncat-1,
theta= linear.starts$beta_intercept + linear.starts$beta_slope*midbins,
sb=EVtrees.bayou[[1]]$sb, loc=EVtrees.bayou[[1]]$loc, t2=EVtrees.bayou[[1]]$t2)
)
plot(midbins, startpar.linearEVD$EV$theta)
## Set priors
EV.fixed <- list(k=length(EVtrees.bayou[[1]]$sb), ntheta=ncat-1, sb=EVtrees.bayou[[1]]$sb, loc=EVtrees.bayou[[1]]$loc, t2=EVtrees.bayou[[1]]$t2)
D.fixed <- list(k=length(Dtrees.bayou[[1]]$sb), ntheta=ncat-1, sb=Dtrees.bayou[[1]]$sb, loc=Dtrees.bayou[[1]]$loc, t2=Dtrees.bayou[[1]]$t2)
prior.stepEVD <- list(
EV = make.prior(tdEV$phy, plot.prior = FALSE,
dists=list(dalpha="dlnorm", dsig2="dhalfcauchy",
dbeta_left="dunif",
dbeta_right="dunif",
dbeta_center="dunif",
dsb="fixed", dk="fixed",
dtheta="dnull", dloc="fixed"),
param=list(dalpha=list(meanlog=1, sdlog=1), dsig2=list(scale=0.1),
dbeta_left=list(min=-12, max=0),
dbeta_right=list(min=-12, max=0),
dbeta_center=list(min=-100, max=100),
dk="fixed",
dsb="fixed",
dtheta=list(), dloc="fixed"),
fixed=EV.fixed),
D = make.prior(tdD$phy, plot.prior = FALSE,
dists=list(dalpha="dlnorm", dsig2="dhalfcauchy",
dbeta_left="dunif",
dbeta_right="dunif",
dbeta_center="dunif",
dsb="fixed", dk="fixed",
dtheta="dnull", dloc="fixed"),
param=list(dalpha=list(meanlog=1, sdlog=1), dsig2=list(scale=0.1),
dbeta_left=list(min=-12, max=0),
dbeta_right=list(min=-12, max=0),
dbeta_center=list(min=-100, max=100),
dk="fixed",
dsb="fixed",
dtheta=list(), dloc="fixed"),
fixed=D.fixed)
)
prior.sigmoidEVD <- list(
EV = make.prior(tdEV$phy, plot.prior = FALSE,
dists=list(dalpha="dlnorm", dsig2="dhalfcauchy",
dbeta_left="dunif",
dbeta_right="dunif",
dbeta_center="dunif",
dbeta_slope="dlnorm",
dsb="fixed", dk="fixed",
dtheta="dnull", dloc="fixed"),
param=list(dalpha=list(meanlog=1, sdlog=1), dsig2=list(scale=0.1),
dbeta_left=list(min=-12, max=0),
dbeta_right=list(min=-12, max=0),
dbeta_center=list(min=-100, max=100),
dbeta_slope=list(meanlog=-3, sdlog=1),
dk="fixed",
dsb="fixed",
dtheta=list(), dloc="fixed"),
fixed=EV.fixed),
D = make.prior(tdD$phy, plot.prior = FALSE,
dists=list(dalpha="dlnorm", dsig2="dhalfcauchy",
dbeta_left="dunif",
dbeta_right="dunif",
dbeta_center="dunif",
dbeta_slope="dlnorm",
dsb="fixed", dk="fixed",
dtheta="dnull", dloc="fixed"),
param=list(dalpha=list(meanlog=1, sdlog=1), dsig2=list(scale=0.1),
dbeta_left=list(min=-12, max=0),
dbeta_right=list(min=-12, max=0),
dbeta_center=list(min=-100, max=100),
dbeta_slope=list(meanlog=-3, sdlog=1),
dk="fixed",
dsb="fixed",
dtheta=list(), dloc="fixed"),
fixed=D.fixed)
)
prior.linearEVD <- list(
EV = make.prior(tdEV$phy, plot.prior = FALSE,
dists=list(dalpha="dlnorm", dsig2="dhalfcauchy",
dbeta_intercept="dnorm",
dbeta_slope="dnorm",
dsb="fixed", dk="fixed",
dtheta="dnull", dloc="fixed"),
param=list(dalpha=list(meanlog=1, sdlog=1), dsig2=list(scale=0.1),
dbeta_intercept=list(mean=-6, sd=1.5),
dbeta_slope=list(mean=0, sd=0.1),
dk="fixed",
dsb="fixed",
dtheta=list(), dloc="fixed"),
fixed=EV.fixed),
D = make.prior(tdD$phy, plot.prior = FALSE,
dists=list(dalpha="dlnorm", dsig2="dhalfcauchy",
dbeta_intercept="dnorm",
dbeta_slope="dnorm",
dsb="fixed", dk="fixed",
dtheta="dnull", dloc="fixed"),
param=list(dalpha=list(meanlog=1, sdlog=1), dsig2=list(scale=0.1),
dbeta_intercept=list(mean=-6, sd=1.5),
dbeta_slope=list(mean=0, sd=0.1),
dk="fixed",
dsb="fixed",
dtheta=list(), dloc="fixed"),
fixed=D.fixed)
)mymcmcEV.step <- bayou.makeMCMC(tdEV$phy, tdEV[[trait]], pred=tdEV['binpred'], SE=0.02, model=model.stepTheta, prior=prior.stepEVD$EV, startpar=startpar.stepEVD$EV, new.dir=TRUE, outname="stepThetaEV_r1", plot.freq=NULL, ticker.freq=1000, samp = 100)
mymcmcD.step <- bayou.makeMCMC(tdD$phy, tdD[[trait]], pred=tdD['binpred'], SE=0.02, model=model.stepTheta, prior=prior.stepEVD$D, startpar=startpar.stepEVD$D, new.dir=TRUE, outname="stepThetaD_r1", plot.freq=NULL, ticker.freq=1000, samp = 100)
mymcmcEV.sigmoid <- bayou.makeMCMC(tdEV$phy, tdEV[[trait]], pred=tdEV['binpred'], SE=0.02, model=model.sigmoidTheta, prior=prior.sigmoidEVD$EV, startpar=startpar.sigmoidEVD$EV, new.dir=TRUE, outname="sigmoidThetaEV_r1", plot.freq=NULL, ticker.freq=1000, samp = 100)
mymcmcD.sigmoid <- bayou.makeMCMC(tdD$phy, tdD[[trait]], pred=tdD['binpred'], SE=0.02, model=model.sigmoidTheta, prior=prior.sigmoidEVD$D, startpar=startpar.sigmoidEVD$D, new.dir=TRUE, outname="sigmoidThetaD_r1", plot.freq=NULL, ticker.freq=1000, samp = 100)
mymcmcEV.linear <- bayou.makeMCMC(tdEV$phy, tdEV[[trait]], pred=tdEV['binpred'], SE=0.02, model=model.linearTheta, prior=prior.linearEVD$EV, startpar=startpar.linearEVD$EV, new.dir=TRUE, outname="linearThetaEV_r1", plot.freq=NULL, ticker.freq=1000, samp = 100)
mymcmcD.linear <- bayou.makeMCMC(tdD$phy, tdD[[trait]], pred=tdD['binpred'], SE=0.02, model=model.linearTheta, prior=prior.linearEVD$D, startpar=startpar.linearEVD$D, new.dir=TRUE, outname="linearThetaD_r1", plot.freq=NULL, ticker.freq=1000, samp = 100)strees.bayou <- EVtrees.bayou
mymcmcEV.step$run(100000)gen lnL prior alpha sig2 beta_le beta_ri beta_ce k .alpha .beta_c .beta_l .beta_r .sig2 .theta
1000 -389.49 -37.84 123.25 367.89 -7.03 -4.87 -25.93 347 0.40 0.89 0.83 0.84 0.36 1.00
2000 -391.58 -43.97 278.06 939.88 -6.87 -5.03 -8.55 339 0.37 0.93 0.83 0.82 0.31 1.00
3000 -392.95 -39.24 148.78 464.69 -6.78 -4.85 -10.20 324 0.35 0.91 0.82 0.82 0.31 1.00
4000 -383.60 -40.84 195.60 511.91 -6.80 -4.58 -11.58 370 0.34 0.91 0.83 0.82 0.32 1.00
5000 -403.00 -40.82 174.01 689.39 -6.66 -4.95 22.47 351 0.33 0.91 0.82 0.83 0.33 1.00
6000 -384.36 -39.70 157.54 507.07 -6.81 -4.59 -6.65 370 0.33 0.92 0.84 0.84 0.33 1.00
7000 -389.76 -43.39 262.23 829.82 -6.70 -4.88 13.86 325 0.33 0.90 0.84 0.83 0.33 1.00
8000 -395.28 -40.45 172.14 588.95 -6.83 -4.85 -19.79 360 0.32 0.91 0.84 0.83 0.32 1.00
9000 -402.95 -45.02 314.84 1115.19 -6.76 -5.07 -9.86 364 0.32 0.91 0.83 0.83 0.32 1.00
10000 -386.46 -43.48 258.10 906.12 -6.78 -4.88 14.43 365 0.32 0.91 0.83 0.83 0.31 1.00
11000 -393.09 -46.57 375.87 1446.57 -6.80 -4.94 24.70 348 0.32 0.91 0.83 0.83 0.31 1.00
12000 -395.50 -40.75 175.73 649.71 -6.99 -5.00 22.85 331 0.32 0.91 0.83 0.83 0.31 1.00
13000 -389.45 -41.84 201.86 779.10 -6.74 -4.76 29.74 326 0.32 0.91 0.83 0.82 0.31 1.00
14000 -390.01 -42.50 231.29 750.37 -6.69 -4.81 19.73 348 0.32 0.91 0.83 0.82 0.31 1.00
15000 -400.90 -42.79 244.14 748.07 -6.66 -4.85 32.51 358 0.32 0.91 0.84 0.82 0.30 1.00
16000 -385.66 -42.80 243.25 760.22 -6.70 -4.75 27.84 332 0.32 0.91 0.84 0.82 0.30 1.00
17000 -400.21 -39.10 139.64 508.42 -7.04 -4.81 15.16 351 0.31 0.91 0.84 0.82 0.30 1.00
18000 -393.84 -42.58 224.19 848.50 -6.49 -4.66 16.79 350 0.31 0.91 0.84 0.82 0.30 1.00
19000 -395.55 -44.98 313.61 1103.84 -6.80 -4.89 15.81 350 0.31 0.91 0.84 0.82 0.30 1.00
20000 -398.85 -39.82 153.06 580.66 -6.89 -4.79 -21.83 342 0.31 0.91 0.84 0.82 0.30 1.00
21000 -396.40 -42.09 209.37 800.72 -6.89 -4.79 9.78 357 0.31 0.92 0.84 0.82 0.29 1.00
22000 -389.66 -44.13 281.43 984.07 -6.78 -4.78 17.18 325 0.31 0.92 0.84 0.83 0.30 1.00
23000 -413.53 -38.11 128.30 380.73 -6.69 -4.83 17.46 352 0.31 0.92 0.84 0.83 0.30 1.00
24000 -398.98 -40.60 176.04 597.82 -6.67 -4.77 -19.42 358 0.31 0.92 0.84 0.83 0.30 1.00
25000 -406.83 -40.81 178.14 644.24 -6.85 -4.81 17.23 333 0.31 0.92 0.84 0.83 0.30 1.00
26000 -391.49 -42.63 232.31 793.36 -6.80 -4.60 10.93 348 0.31 0.92 0.84 0.83 0.30 1.00
27000 -406.05 -39.63 148.27 571.46 -6.69 -4.93 -6.03 364 0.31 0.92 0.84 0.83 0.30 1.00
28000 -392.42 -41.14 198.64 571.78 -6.90 -5.13 -20.97 353 0.31 0.92 0.84 0.83 0.30 1.00
29000 -400.75 -40.46 169.53 616.89 -6.85 -4.79 4.53 333 0.31 0.92 0.84 0.83 0.30 1.00
30000 -393.68 -40.38 176.09 535.25 -6.79 -4.94 11.73 313 0.31 0.92 0.84 0.83 0.30 1.00
31000 -392.74 -41.88 222.37 612.96 -6.79 -4.83 21.27 341 0.31 0.92 0.84 0.83 0.30 1.00
32000 -385.88 -41.96 223.52 629.56 -6.77 -4.78 -18.64 361 0.31 0.92 0.84 0.83 0.30 1.00
33000 -401.70 -39.50 140.93 606.62 -6.89 -4.68 -18.78 339 0.31 0.92 0.84 0.83 0.30 1.00
34000 -387.65 -41.48 207.75 601.08 -6.90 -4.68 -21.06 365 0.31 0.92 0.84 0.83 0.30 1.00
35000 -393.17 -41.79 216.03 634.00 -6.78 -4.77 26.11 332 0.31 0.92 0.84 0.83 0.30 1.00
36000 -388.83 -41.93 206.06 769.54 -6.80 -4.83 -1.35 330 0.31 0.92 0.84 0.83 0.30 1.00
37000 -397.23 -42.16 215.54 765.58 -6.84 -4.44 -14.63 342 0.31 0.92 0.84 0.83 0.30 1.00
38000 -408.26 -38.70 133.86 462.40 -6.70 -4.92 23.39 333 0.31 0.92 0.84 0.83 0.30 1.00
39000 -386.80 -40.06 166.35 529.57 -6.79 -4.59 -30.30 327 0.31 0.92 0.84 0.83 0.30 1.00
40000 -401.33 -44.19 294.21 895.00 -6.52 -4.99 31.11 358 0.31 0.92 0.84 0.83 0.30 1.00
41000 -393.62 -42.32 219.07 796.21 -6.68 -4.80 -3.42 348 0.31 0.92 0.84 0.83 0.30 1.00
42000 -394.74 -40.80 182.10 605.73 -6.79 -4.73 -15.75 360 0.31 0.92 0.84 0.83 0.30 1.00
43000 -384.84 -43.36 266.37 780.68 -6.79 -4.77 -1.18 332 0.31 0.92 0.84 0.83 0.30 1.00
44000 -378.56 -45.21 333.69 1035.86 -6.72 -4.79 3.28 370 0.31 0.92 0.84 0.83 0.30 1.00
45000 -396.24 -46.72 400.07 1290.01 -6.95 -4.68 -7.82 336 0.31 0.92 0.84 0.83 0.30 1.00
46000 -393.99 -40.11 165.30 550.41 -6.71 -4.96 4.05 375 0.31 0.92 0.84 0.83 0.30 1.00
47000 -388.50 -42.13 216.93 740.60 -6.65 -4.93 2.32 349 0.31 0.92 0.84 0.83 0.30 1.00
48000 -397.35 -44.46 305.00 925.52 -6.75 -5.04 -24.27 333 0.31 0.92 0.84 0.83 0.30 1.00
49000 -406.00 -42.87 232.42 892.00 -6.89 -4.94 -3.16 378 0.31 0.92 0.84 0.83 0.29 1.00
50000 -395.63 -49.18 542.50 1732.73 -6.62 -4.70 26.58 326 0.31 0.92 0.84 0.83 0.29 1.00
51000 -395.34 -41.23 184.91 722.11 -6.74 -4.87 4.33 346 0.31 0.92 0.84 0.83 0.29 1.00
52000 -393.86 -43.92 275.44 942.99 -6.75 -5.01 -2.60 357 0.31 0.92 0.84 0.83 0.29 1.00
53000 -402.29 -38.98 131.80 552.64 -6.73 -4.99 -16.72 333 0.31 0.92 0.84 0.83 0.29 1.00
54000 -390.22 -39.02 145.83 437.62 -6.76 -4.85 -4.37 361 0.31 0.92 0.84 0.83 0.29 1.00
55000 -393.85 -41.81 207.54 711.17 -6.90 -4.83 31.00 332 0.31 0.93 0.84 0.83 0.29 1.00
56000 -389.99 -46.97 410.00 1361.11 -6.84 -4.76 -24.89 375 0.31 0.93 0.84 0.83 0.29 1.00
57000 -390.06 -44.38 295.97 966.77 -6.89 -4.68 -5.27 344 0.31 0.93 0.84 0.83 0.29 1.00
58000 -390.89 -43.05 244.40 848.59 -6.77 -4.72 -12.91 341 0.31 0.93 0.84 0.83 0.29 1.00
59000 -395.00 -48.49 478.47 1816.41 -6.84 -4.73 17.38 360 0.31 0.92 0.84 0.83 0.29 1.00
60000 -397.08 -41.51 198.91 687.09 -7.10 -4.75 19.48 346 0.31 0.93 0.84 0.83 0.29 1.00
61000 -395.70 -42.86 252.39 706.34 -6.79 -4.98 24.44 350 0.31 0.93 0.84 0.83 0.30 1.00
62000 -386.34 -44.78 309.56 1040.87 -6.72 -4.91 22.96 330 0.31 0.93 0.84 0.83 0.30 1.00
63000 -385.14 -43.25 256.21 825.96 -6.70 -4.85 20.95 334 0.31 0.93 0.84 0.83 0.30 1.00
64000 -402.62 -41.89 221.60 620.55 -6.76 -4.74 28.11 352 0.31 0.93 0.84 0.83 0.30 1.00
65000 -396.89 -44.53 304.26 961.51 -6.54 -4.79 20.47 320 0.31 0.93 0.84 0.83 0.30 1.00
66000 -378.04 -45.15 334.87 995.20 -6.75 -4.77 -8.92 370 0.31 0.93 0.84 0.83 0.29 1.00
67000 -393.12 -41.94 209.97 737.75 -6.56 -4.68 11.97 375 0.31 0.92 0.84 0.83 0.30 1.00
68000 -390.80 -41.78 212.03 661.95 -6.87 -4.85 15.86 324 0.31 0.92 0.84 0.83 0.29 1.00
69000 -397.92 -39.92 165.58 500.23 -6.62 -4.64 15.76 343 0.31 0.92 0.84 0.83 0.29 1.00
70000 -404.76 -45.58 339.91 1185.09 -6.60 -4.69 -23.19 333 0.31 0.92 0.84 0.83 0.29 1.00
71000 -386.45 -41.38 208.54 565.43 -6.86 -4.84 6.24 327 0.31 0.92 0.84 0.83 0.29 1.00
72000 -401.90 -39.05 136.13 528.74 -6.84 -4.68 9.90 342 0.31 0.92 0.84 0.83 0.29 1.00
73000 -414.37 -36.12 89.74 325.58 -6.58 -5.16 22.22 333 0.31 0.92 0.84 0.83 0.29 1.00
74000 -389.69 -44.12 293.98 864.97 -6.76 -4.86 17.09 344 0.31 0.92 0.84 0.83 0.29 1.00
75000 -389.78 -42.20 214.08 795.76 -6.75 -4.88 -17.28 353 0.31 0.92 0.84 0.83 0.29 1.00
76000 -391.23 -41.09 187.42 650.00 -6.84 -4.59 -14.33 353 0.31 0.92 0.84 0.83 0.29 1.00
77000 -391.60 -41.85 199.04 813.63 -6.75 -4.97 -23.95 365 0.31 0.92 0.84 0.83 0.29 1.00
78000 -379.49 -47.37 431.10 1427.52 -6.86 -4.97 -26.45 370 0.31 0.92 0.84 0.83 0.29 1.00
79000 -384.61 -46.11 372.18 1180.43 -6.64 -4.88 31.74 334 0.31 0.92 0.84 0.83 0.29 1.00
80000 -384.44 -47.68 442.42 1544.49 -6.75 -4.77 -8.00 334 0.31 0.92 0.84 0.83 0.29 1.00
81000 -389.82 -40.40 175.85 544.47 -6.85 -4.96 2.74 353 0.31 0.92 0.84 0.83 0.29 1.00
82000 -387.05 -42.59 238.91 720.25 -6.79 -4.60 -21.93 349 0.31 0.92 0.84 0.83 0.29 1.00
83000 -391.86 -44.26 282.39 1040.74 -6.95 -4.88 13.49 360 0.31 0.92 0.84 0.83 0.29 1.00
84000 -390.86 -40.65 177.18 605.51 -6.71 -4.71 -1.73 353 0.31 0.92 0.84 0.83 0.29 1.00
85000 -394.09 -40.74 183.19 580.14 -6.91 -4.80 0.43 332 0.31 0.92 0.84 0.83 0.29 1.00
86000 -393.36 -40.68 178.91 597.94 -6.96 -4.95 -21.63 348 0.31 0.92 0.84 0.83 0.29 1.00
87000 -409.43 -37.88 113.33 457.79 -6.57 -5.13 -23.85 378 0.31 0.92 0.84 0.83 0.29 1.00
88000 -397.91 -41.66 208.74 650.50 -6.67 -5.12 -15.26 357 0.31 0.92 0.84 0.83 0.29 1.00
89000 -403.88 -43.81 265.21 991.54 -6.61 -4.68 -17.81 364 0.31 0.92 0.84 0.83 0.29 1.00
90000 -390.30 -40.00 168.48 496.10 -6.79 -4.74 10.70 371 0.31 0.92 0.84 0.83 0.29 1.00
91000 -393.33 -39.93 156.78 574.93 -6.74 -4.74 1.36 348 0.31 0.92 0.84 0.83 0.29 1.00
92000 -392.35 -48.18 512.55 1254.55 -6.75 -4.96 -5.62 339 0.31 0.92 0.84 0.83 0.29 1.00
93000 -392.23 -42.94 239.62 849.17 -6.77 -5.10 -7.29 342 0.31 0.92 0.84 0.83 0.29 1.00
94000 -403.08 -45.86 339.64 1367.28 -6.48 -4.85 14.54 364 0.31 0.92 0.84 0.83 0.29 1.00
95000 -401.07 -39.77 150.27 591.64 -6.96 -4.85 21.10 339 0.31 0.92 0.84 0.83 0.29 1.00
96000 -391.10 -46.58 384.20 1360.43 -6.71 -4.81 -24.22 332 0.31 0.92 0.84 0.83 0.29 1.00
97000 -399.44 -41.16 188.94 657.80 -6.70 -5.00 -12.29 342 0.31 0.92 0.84 0.83 0.29 1.00
98000 -386.43 -40.75 186.23 557.66 -6.92 -4.74 1.27 346 0.31 0.93 0.84 0.83 0.29 1.00
99000 -404.54 -38.42 131.17 422.74 -6.83 -4.90 -8.60 321 0.31 0.93 0.84 0.83 0.29 1.00
100000 -402.55 -43.67 261.22 965.18 -6.60 -4.84 19.49 364 0.31 0.93 0.84 0.83 0.29 1.00 chainEV.step <- mymcmcEV.step$load()
chainEV.step <- set.burnin(chainEV.step, 0.3)
plot(chainEV.step)
strees.bayou <- Dtrees.bayou
mymcmcD.step$run(100000)gen lnL prior alpha sig2 beta_le beta_ri beta_ce k .alpha .beta_c .beta_l .beta_r .sig2 .theta
1000 -171.01 -25.69 15.15 43.81 -6.74 -4.35 -96.48 423 0.55 0.98 0.91 0.93 0.37 1.00
2000 -174.17 -24.39 10.40 36.71 -6.07 -4.48 13.69 423 0.53 0.97 0.89 0.94 0.35 1.00
3000 -177.91 -23.60 9.21 28.46 -6.30 -4.49 -77.11 352 0.49 0.97 0.87 0.93 0.36 1.00
4000 -177.86 -24.82 12.69 35.63 -6.05 -5.00 -62.49 372 0.48 0.93 0.87 0.92 0.37 1.00
5000 -165.96 -25.87 17.34 39.70 -6.70 -4.49 -72.05 390 0.48 0.93 0.88 0.92 0.37 1.00
6000 -177.56 -23.84 8.79 33.75 -5.68 -3.78 -1.20 396 0.47 0.93 0.88 0.91 0.38 1.00
7000 -183.75 -23.20 6.77 31.93 -6.13 -3.79 60.75 437 0.47 0.93 0.88 0.92 0.38 1.00
8000 -183.96 -24.31 11.27 32.03 -5.38 -3.66 59.95 390 0.48 0.93 0.88 0.93 0.38 1.00
9000 -178.99 -23.83 9.35 31.28 -5.87 -3.97 91.09 404 0.48 0.93 0.88 0.93 0.38 1.00
10000 -175.59 -25.05 12.64 40.35 -6.05 -4.12 -27.01 372 0.48 0.93 0.88 0.93 0.39 1.00
11000 -178.08 -27.55 21.27 67.81 -5.91 -4.17 -27.87 391 0.48 0.92 0.88 0.93 0.39 1.00
12000 -179.36 -24.02 8.60 37.84 -5.78 -4.94 -56.76 379 0.48 0.93 0.88 0.93 0.39 1.00
13000 -177.17 -25.21 12.54 44.00 -5.67 -4.47 60.52 388 0.48 0.93 0.89 0.93 0.39 1.00
14000 -177.63 -24.62 12.36 33.44 -5.89 -3.76 69.03 409 0.48 0.93 0.89 0.93 0.39 1.00
15000 -175.11 -27.62 21.29 70.33 -6.21 -4.73 -69.72 374 0.48 0.93 0.90 0.93 0.39 1.00
16000 -181.41 -24.02 8.85 36.57 -6.74 -4.62 -67.26 420 0.48 0.93 0.90 0.93 0.39 1.00
17000 -177.04 -24.80 11.82 38.67 -6.22 -4.71 -86.54 409 0.48 0.93 0.89 0.93 0.40 1.00
18000 -177.77 -22.22 5.86 22.40 -6.75 -3.87 -64.37 398 0.48 0.93 0.90 0.93 0.40 1.00
19000 -172.44 -25.47 13.64 45.03 -6.53 -4.55 -38.13 357 0.48 0.93 0.90 0.93 0.40 1.00
20000 -176.64 -24.11 9.72 34.52 -6.46 -4.58 -79.74 372 0.48 0.93 0.90 0.93 0.40 1.00
21000 -175.20 -24.51 11.37 35.00 -6.01 -4.57 -4.09 403 0.48 0.94 0.90 0.93 0.40 1.00
22000 -178.86 -23.34 8.16 28.52 -6.11 -4.12 33.78 386 0.47 0.94 0.90 0.93 0.40 1.00
23000 -176.80 -23.69 8.87 30.92 -6.48 -3.87 15.17 406 0.47 0.94 0.90 0.93 0.40 1.00
24000 -182.82 -23.11 7.92 26.17 -5.61 -3.64 93.69 435 0.47 0.94 0.90 0.93 0.40 1.00
25000 -186.23 -22.92 5.70 32.62 -5.29 -2.88 57.97 392 0.47 0.94 0.90 0.93 0.40 1.00
26000 -179.14 -23.50 7.91 31.74 -5.68 -4.16 42.02 366 0.47 0.94 0.90 0.93 0.40 1.00
27000 -177.35 -23.01 7.57 26.05 -5.67 -4.59 20.69 381 0.47 0.94 0.90 0.93 0.40 1.00
28000 -179.55 -22.86 6.56 27.85 -5.87 -3.95 4.11 401 0.47 0.94 0.90 0.93 0.40 1.00
29000 -182.47 -22.01 5.71 20.58 -5.69 -3.90 95.86 391 0.48 0.94 0.90 0.93 0.40 1.00
30000 -181.10 -23.45 7.05 34.84 -5.85 -4.65 68.03 379 0.48 0.94 0.90 0.93 0.40 1.00
31000 -180.90 -22.87 6.21 29.34 -6.03 -4.48 29.61 374 0.48 0.94 0.90 0.93 0.40 1.00
32000 -184.70 -26.22 16.00 52.82 -5.82 -4.13 -3.89 404 0.48 0.94 0.90 0.93 0.40 1.00
33000 -177.09 -25.42 14.86 39.21 -6.77 -5.19 -98.59 374 0.48 0.93 0.90 0.93 0.40 1.00
34000 -179.83 -22.47 5.60 26.34 -7.11 -4.84 -71.14 394 0.47 0.93 0.90 0.93 0.40 1.00
35000 -178.03 -25.43 14.31 41.53 -6.15 -4.53 -86.83 391 0.47 0.93 0.90 0.93 0.40 1.00
36000 -181.82 -23.85 8.41 35.59 -6.08 -5.25 -44.11 391 0.47 0.93 0.90 0.93 0.40 1.00
37000 -179.72 -27.98 22.47 77.30 -6.17 -4.07 3.13 383 0.47 0.93 0.90 0.93 0.40 1.00
38000 -179.67 -26.44 15.93 59.47 -5.96 -4.50 -30.31 403 0.47 0.93 0.90 0.93 0.40 1.00
39000 -177.94 -23.52 7.91 32.23 -5.93 -4.50 0.86 380 0.47 0.93 0.90 0.93 0.40 1.00
40000 -180.59 -23.09 7.30 28.20 -5.40 -4.64 80.16 403 0.47 0.93 0.90 0.93 0.40 1.00
41000 -183.54 -24.52 11.30 35.50 -5.31 -4.13 51.20 426 0.47 0.93 0.90 0.93 0.40 1.00
42000 -179.63 -24.34 10.41 35.69 -6.52 -4.58 -56.26 370 0.47 0.93 0.90 0.93 0.40 1.00
43000 -177.41 -25.15 14.15 36.50 -6.11 -4.26 -22.26 404 0.47 0.93 0.90 0.93 0.40 1.00
44000 -177.70 -24.00 9.03 35.58 -6.29 -4.53 -59.78 381 0.47 0.93 0.90 0.93 0.40 1.00
45000 -179.94 -24.58 11.74 34.86 -5.80 -4.80 -7.82 394 0.47 0.93 0.90 0.93 0.40 1.00
46000 -183.01 -24.93 11.66 41.83 -5.69 -4.16 7.71 407 0.47 0.93 0.90 0.93 0.41 1.00
47000 -173.66 -24.58 12.76 31.52 -5.79 -4.26 -1.65 398 0.47 0.93 0.90 0.93 0.41 1.00
48000 -175.61 -28.05 23.34 75.34 -6.39 -4.32 -28.94 389 0.47 0.93 0.90 0.93 0.41 1.00
49000 -175.88 -23.43 7.46 32.53 -6.16 -4.64 -88.48 357 0.47 0.93 0.90 0.93 0.41 1.00
50000 -179.32 -24.21 10.05 34.83 -5.98 -4.56 -29.40 426 0.47 0.93 0.90 0.93 0.41 1.00
51000 -181.49 -24.65 10.91 39.46 -5.84 -4.18 12.50 383 0.47 0.93 0.90 0.93 0.41 1.00
52000 -172.94 -25.75 14.39 48.27 -6.53 -4.77 -90.18 389 0.47 0.93 0.90 0.93 0.41 1.00
53000 -183.86 -22.98 7.08 27.42 -6.61 -4.88 -40.08 391 0.47 0.93 0.90 0.93 0.41 1.00
54000 -180.74 -23.91 10.59 28.26 -6.28 -4.10 -4.94 384 0.47 0.93 0.90 0.93 0.41 1.00
55000 -183.33 -24.70 11.92 36.34 -5.73 -3.97 93.91 407 0.47 0.93 0.90 0.93 0.41 1.00
56000 -178.05 -24.48 10.18 39.29 -5.50 -4.48 8.34 369 0.47 0.93 0.90 0.93 0.41 1.00
57000 -178.65 -23.70 8.56 32.29 -6.03 -3.91 70.14 403 0.47 0.93 0.90 0.93 0.41 1.00
58000 -173.95 -23.37 8.21 28.62 -5.60 -3.37 52.31 381 0.47 0.93 0.90 0.93 0.41 1.00
59000 -181.67 -22.09 5.39 22.50 -6.07 -3.73 83.62 406 0.47 0.93 0.90 0.93 0.41 1.00
60000 -172.09 -26.08 16.23 48.36 -6.65 -4.41 -76.53 369 0.47 0.93 0.90 0.93 0.41 1.00
61000 -174.23 -23.36 8.82 26.48 -6.78 -4.48 -44.25 366 0.47 0.93 0.90 0.93 0.41 1.00
62000 -180.06 -26.81 19.72 52.55 -5.88 -4.17 -0.57 357 0.47 0.93 0.90 0.93 0.41 1.00
63000 -180.14 -24.18 9.61 36.22 -5.59 -4.29 62.08 390 0.47 0.93 0.90 0.93 0.41 1.00
64000 -184.30 -26.12 18.26 41.64 -5.97 -3.68 4.83 384 0.47 0.93 0.90 0.93 0.41 1.00
65000 -180.73 -24.13 9.14 37.33 -5.46 -4.67 -27.27 408 0.47 0.93 0.89 0.93 0.41 1.00
66000 -183.62 -23.22 7.80 28.03 -6.47 -3.18 34.87 366 0.47 0.93 0.89 0.93 0.41 1.00
67000 -176.80 -24.55 12.34 32.30 -6.10 -4.16 0.68 408 0.47 0.93 0.89 0.93 0.41 1.00
68000 -177.00 -24.64 12.48 33.38 -5.72 -4.56 -2.47 357 0.47 0.93 0.89 0.93 0.41 1.00
69000 -182.43 -22.75 5.56 30.55 -5.94 -4.79 -84.40 403 0.47 0.93 0.89 0.93 0.41 1.00
70000 -173.31 -28.06 23.71 73.79 -6.42 -4.62 -71.27 404 0.47 0.93 0.89 0.93 0.41 1.00
71000 -181.27 -22.27 5.56 24.05 -6.32 -4.83 12.58 380 0.47 0.93 0.89 0.93 0.41 1.00
72000 -177.52 -25.15 12.34 43.57 -6.10 -4.99 -77.91 387 0.47 0.94 0.89 0.93 0.41 1.00
73000 -178.40 -23.44 9.10 26.57 -6.30 -4.59 -56.17 352 0.47 0.94 0.90 0.93 0.41 1.00
74000 -178.22 -24.88 11.10 43.51 -6.13 -5.05 -70.99 412 0.47 0.94 0.90 0.93 0.41 1.00
75000 -174.84 -25.68 14.21 47.29 -6.26 -4.46 -56.01 396 0.47 0.94 0.90 0.93 0.41 1.00
76000 -181.69 -25.26 10.83 53.97 -5.94 -4.56 12.89 352 0.47 0.94 0.90 0.93 0.41 1.00
77000 -178.65 -23.94 9.32 33.32 -5.89 -5.05 -56.78 392 0.47 0.94 0.90 0.93 0.41 1.00
78000 -180.42 -26.41 15.44 61.15 -6.16 -5.06 -29.35 366 0.47 0.94 0.90 0.93 0.41 1.00
79000 -178.44 -23.30 8.95 25.22 -6.27 -5.02 -88.35 379 0.47 0.94 0.90 0.93 0.41 1.00
80000 -176.94 -25.68 13.28 51.90 -6.10 -4.61 -65.19 383 0.47 0.94 0.90 0.93 0.41 1.00
81000 -177.49 -25.70 14.69 45.76 -5.95 -4.83 -60.95 393 0.47 0.94 0.90 0.93 0.41 1.00
82000 -179.53 -22.95 6.60 28.97 -6.80 -4.89 -87.57 404 0.47 0.94 0.90 0.93 0.41 1.00
83000 -178.96 -25.09 12.90 40.10 -6.44 -4.66 -14.40 412 0.47 0.94 0.90 0.93 0.41 1.00
84000 -172.35 -26.79 19.27 53.92 -6.35 -4.70 -81.40 394 0.47 0.94 0.90 0.93 0.41 1.00
85000 -182.17 -22.27 5.57 23.97 -6.72 -4.16 -54.86 392 0.47 0.94 0.90 0.93 0.41 1.00
86000 -179.01 -24.97 11.98 41.39 -5.96 -5.15 -34.18 399 0.47 0.94 0.90 0.93 0.41 1.00
87000 -175.74 -23.80 9.87 29.02 -6.54 -4.77 -94.20 391 0.47 0.94 0.90 0.93 0.41 1.00
88000 -179.49 -25.86 14.54 50.29 -5.92 -5.12 -71.84 404 0.47 0.94 0.90 0.93 0.41 1.00
89000 -174.97 -25.66 13.99 47.98 -6.42 -4.52 -44.37 379 0.47 0.94 0.90 0.93 0.41 1.00
90000 -179.57 -24.83 11.88 38.93 -6.20 -4.41 25.35 381 0.47 0.94 0.90 0.93 0.41 1.00
91000 -179.47 -24.29 9.44 38.93 -5.90 -4.65 89.30 415 0.47 0.94 0.90 0.93 0.41 1.00
92000 -180.13 -23.30 6.81 33.37 -6.01 -4.03 -0.48 390 0.47 0.94 0.90 0.93 0.41 1.00
93000 -179.58 -27.28 20.83 61.13 -5.94 -4.76 -46.65 357 0.47 0.94 0.90 0.93 0.41 1.00
94000 -179.80 -24.16 10.48 32.48 -6.49 -4.15 -81.58 352 0.47 0.94 0.90 0.93 0.41 1.00
95000 -177.91 -23.94 8.95 34.82 -6.43 -5.20 -69.95 379 0.47 0.94 0.90 0.93 0.41 1.00
96000 -174.04 -24.18 11.24 30.16 -6.37 -4.81 -79.04 408 0.47 0.94 0.90 0.93 0.41 1.00
97000 -178.15 -23.79 8.78 32.96 -6.33 -4.79 -5.54 366 0.47 0.94 0.90 0.93 0.41 1.00
98000 -181.99 -22.69 5.95 27.90 -5.34 -4.72 -8.72 404 0.47 0.94 0.90 0.93 0.41 1.00
99000 -175.61 -23.08 7.02 29.07 -6.06 -3.91 11.24 407 0.47 0.94 0.90 0.93 0.41 1.00
100000 -183.60 -21.18 3.90 18.25 -5.94 -3.13 -27.03 411 0.47 0.94 0.90 0.93 0.41 1.00 chainD.step <- mymcmcD.step$load()
chainD.step <- set.burnin(chainD.step, 0.3)
plot(chainD.step)
strees.bayou <- EVtrees.bayou
mymcmcEV.sigmoid$run(100000)gen lnL prior alpha sig2 beta_le beta_ri beta_ce beta_sl k .alpha .beta_c .beta_l .beta_r .beta_s .sig2 .theta
1000 -381.94 -44.78 434.51 1320.44 -2.01 -5.52 86.13 0.01 343 0.47 0.83 0.84 0.85 0.73 0.39 1.00
2000 -385.74 -39.95 217.74 756.84 -1.57 -6.82 80.25 0.01 369 0.38 0.77 0.80 0.85 0.59 0.36 1.00
3000 -378.12 -38.91 206.87 533.67 -1.74 -6.66 66.78 0.01 314 0.34 0.75 0.79 0.84 0.54 0.35 1.00
4000 -369.24 -44.48 432.56 1020.03 -1.43 -8.05 18.34 0.01 375 0.32 0.75 0.79 0.84 0.50 0.35 1.00
5000 -384.94 -36.97 149.32 412.83 -0.99 -8.50 63.16 0.01 375 0.32 0.74 0.78 0.84 0.47 0.34 1.00
6000 -366.57 -39.91 229.47 642.41 -1.04 -8.58 39.82 0.01 347 0.30 0.73 0.78 0.84 0.44 0.34 1.00
7000 -383.51 -46.14 467.23 1541.03 -0.19 -8.85 98.80 0.01 336 0.30 0.72 0.77 0.84 0.42 0.33 1.00
8000 -374.70 -42.44 298.88 874.87 -0.21 -9.49 78.04 0.01 364 0.31 0.71 0.76 0.84 0.40 0.32 1.00
9000 -373.87 -40.79 241.31 783.22 -0.26 -10.16 39.10 0.01 347 0.31 0.72 0.76 0.84 0.38 0.32 1.00
10000 -371.61 -44.54 378.26 1145.61 -0.21 -10.12 58.78 0.00 353 0.31 0.72 0.75 0.84 0.37 0.32 1.00
11000 -378.59 -40.44 233.13 660.72 -0.52 -10.77 -1.15 0.01 344 0.31 0.71 0.75 0.85 0.36 0.32 1.00
12000 -380.84 -46.72 490.15 1521.05 -0.67 -10.84 -34.50 0.00 364 0.30 0.71 0.75 0.85 0.36 0.31 1.00
13000 -373.32 -42.69 317.55 742.27 -0.58 -10.44 16.78 0.00 348 0.30 0.71 0.75 0.85 0.35 0.31 1.00
14000 -367.79 -44.31 376.06 1158.79 -0.13 -10.04 68.00 0.01 330 0.31 0.71 0.75 0.85 0.34 0.31 1.00
15000 -370.96 -40.14 231.31 611.28 -0.55 -9.55 48.99 0.01 329 0.31 0.71 0.75 0.86 0.33 0.30 1.00
16000 -375.12 -42.32 294.23 897.33 -1.18 -9.59 -12.04 0.01 338 0.31 0.71 0.75 0.86 0.33 0.30 1.00
17000 -383.17 -36.13 123.94 387.67 -1.43 -9.64 -23.03 0.01 364 0.31 0.70 0.74 0.86 0.33 0.30 1.00
18000 -377.07 -41.40 265.71 808.54 -1.61 -8.90 -14.00 0.01 375 0.31 0.71 0.75 0.86 0.33 0.30 1.00
19000 -374.82 -38.16 178.75 464.33 -1.64 -8.44 2.99 0.01 342 0.31 0.70 0.75 0.86 0.33 0.30 1.00
20000 -373.36 -40.48 243.97 713.56 -1.49 -8.17 17.81 0.01 334 0.31 0.70 0.75 0.86 0.33 0.30 1.00
21000 -370.91 -41.89 304.73 810.19 -3.00 -7.55 -57.26 0.01 347 0.31 0.70 0.75 0.86 0.33 0.30 1.00
22000 -376.60 -42.48 319.86 952.73 -3.07 -7.37 -63.78 0.01 375 0.31 0.70 0.75 0.86 0.33 0.30 1.00
23000 -379.68 -39.51 210.06 656.10 -1.85 -7.75 9.35 0.01 375 0.31 0.70 0.75 0.86 0.33 0.30 1.00
24000 -377.61 -41.62 285.88 826.90 -1.26 -7.21 79.59 0.01 375 0.31 0.69 0.75 0.86 0.33 0.30 1.00
25000 -380.41 -41.28 272.84 717.47 -1.16 -8.01 37.46 0.01 323 0.31 0.69 0.75 0.86 0.33 0.30 1.00
26000 -376.76 -43.34 352.88 1080.64 -0.96 -7.45 92.70 0.01 339 0.31 0.69 0.75 0.85 0.33 0.30 1.00
27000 -376.33 -42.61 322.71 897.90 -1.64 -7.58 23.84 0.01 361 0.31 0.69 0.75 0.85 0.33 0.31 1.00
28000 -386.15 -43.03 342.29 812.92 -2.34 -8.08 -39.18 0.01 321 0.31 0.69 0.75 0.85 0.33 0.31 1.00
29000 -376.79 -41.15 266.32 741.19 -2.32 -7.70 -25.77 0.01 325 0.31 0.69 0.75 0.85 0.33 0.31 1.00
30000 -388.41 -43.88 384.61 985.88 -2.63 -7.15 -35.71 0.01 310 0.30 0.69 0.76 0.85 0.34 0.31 1.00
31000 -376.92 -48.99 685.50 1945.54 -2.63 -7.92 -67.75 0.01 344 0.31 0.68 0.76 0.85 0.34 0.31 1.00
32000 -366.26 -45.32 469.32 1175.71 -2.70 -8.31 -69.93 0.01 368 0.31 0.69 0.76 0.86 0.34 0.31 1.00
33000 -378.60 -38.94 199.95 582.00 -2.94 -8.03 -71.70 0.01 367 0.31 0.68 0.76 0.86 0.34 0.31 1.00
34000 -373.92 -43.79 395.69 980.85 -2.70 -7.54 -42.63 0.01 353 0.31 0.68 0.76 0.86 0.34 0.31 1.00
35000 -363.76 -42.98 337.45 932.52 -2.61 -8.85 -81.21 0.01 332 0.30 0.69 0.76 0.86 0.33 0.31 1.00
36000 -388.09 -39.78 206.63 664.83 -1.82 -9.30 -46.73 0.01 310 0.30 0.69 0.76 0.86 0.33 0.31 1.00
37000 -375.91 -43.81 358.60 1035.42 -1.70 -8.64 -11.89 0.01 360 0.30 0.69 0.76 0.86 0.33 0.31 1.00
38000 -378.57 -40.23 247.19 618.57 -2.54 -7.33 -36.81 0.01 364 0.30 0.69 0.76 0.86 0.33 0.31 1.00
39000 -369.74 -43.67 372.60 995.49 -2.92 -7.12 -52.95 0.01 332 0.30 0.69 0.76 0.86 0.33 0.31 1.00
40000 -374.96 -41.78 287.08 949.12 -3.51 -6.77 -64.98 0.01 339 0.30 0.69 0.76 0.86 0.33 0.31 1.00
41000 -370.15 -40.56 258.74 632.09 -2.63 -7.23 -31.18 0.01 332 0.30 0.69 0.76 0.86 0.34 0.30 1.00
42000 -372.65 -38.93 209.07 560.99 -2.85 -6.53 -10.56 0.01 327 0.30 0.69 0.76 0.86 0.34 0.30 1.00
43000 -383.06 -45.44 448.36 1539.55 -2.82 -6.31 -4.07 0.01 333 0.30 0.69 0.76 0.86 0.34 0.30 1.00
44000 -371.88 -37.40 170.56 455.52 -2.42 -5.71 47.97 0.01 332 0.30 0.69 0.76 0.86 0.35 0.30 1.00
45000 -376.17 -40.87 270.89 694.75 -3.25 -6.38 -40.17 0.01 360 0.30 0.69 0.76 0.86 0.35 0.30 1.00
46000 -373.97 -41.95 307.28 858.50 -3.60 -5.45 -40.75 0.01 343 0.30 0.69 0.75 0.86 0.35 0.30 1.00
47000 -380.72 -39.98 237.09 719.98 -3.87 -4.48 -19.18 0.02 352 0.30 0.69 0.76 0.86 0.36 0.30 1.00
48000 -384.12 -39.43 215.56 702.13 -4.32 -3.38 -7.92 0.02 351 0.30 0.69 0.76 0.87 0.37 0.30 1.00
49000 -379.65 -38.70 203.94 566.72 -4.65 -3.48 -42.85 0.02 320 0.30 0.69 0.76 0.87 0.38 0.30 1.00
50000 -371.17 -40.06 231.70 758.87 -4.53 -3.22 -29.57 0.03 368 0.30 0.69 0.76 0.87 0.39 0.30 1.00
51000 -382.73 -41.27 276.20 895.22 -4.68 -3.18 -30.31 0.02 367 0.30 0.69 0.76 0.87 0.40 0.30 1.00
52000 -368.42 -40.45 259.97 692.32 -4.31 -2.86 11.49 0.02 376 0.30 0.69 0.76 0.87 0.40 0.30 1.00
53000 -371.85 -45.88 496.40 1599.39 -4.58 -3.76 -48.66 0.02 327 0.30 0.69 0.76 0.87 0.41 0.30 1.00
54000 -360.76 -45.86 516.50 1401.02 -3.99 -4.33 -17.25 0.02 348 0.30 0.69 0.76 0.87 0.42 0.30 1.00
55000 -375.62 -43.18 353.74 1144.17 -4.11 -3.67 -12.50 0.02 342 0.30 0.69 0.76 0.87 0.42 0.30 1.00
56000 -359.55 -40.87 276.21 736.04 -4.53 -3.63 -41.55 0.02 375 0.30 0.69 0.76 0.87 0.43 0.30 1.00
57000 -372.44 -37.10 166.01 428.95 -4.64 -3.12 -21.01 0.02 348 0.30 0.69 0.76 0.87 0.44 0.30 1.00
58000 -372.11 -39.11 214.33 614.19 -4.17 -3.46 -6.37 0.02 346 0.30 0.69 0.76 0.87 0.44 0.30 1.00
59000 -381.21 -40.39 244.27 773.60 -4.26 -3.06 1.70 0.03 313 0.30 0.68 0.76 0.87 0.45 0.30 1.00
60000 -378.88 -38.93 209.30 569.08 -4.42 -2.85 8.91 0.03 350 0.30 0.68 0.75 0.87 0.46 0.30 1.00
61000 -376.40 -41.93 307.76 889.65 -4.54 -3.08 -11.80 0.03 338 0.30 0.68 0.76 0.87 0.47 0.30 1.00
62000 -376.76 -42.03 305.54 918.96 -4.59 -2.92 -21.48 0.03 364 0.30 0.68 0.76 0.87 0.47 0.30 1.00
63000 -379.17 -37.02 144.72 534.15 -4.58 -3.23 -15.82 0.04 353 0.30 0.68 0.76 0.87 0.48 0.30 1.00
64000 -378.55 -41.48 288.36 882.06 -4.34 -3.28 -0.25 0.02 336 0.30 0.68 0.76 0.86 0.49 0.30 1.00
65000 -362.49 -38.50 203.85 518.52 -4.13 -4.11 -23.33 0.02 346 0.30 0.68 0.76 0.86 0.49 0.30 1.00
66000 -365.48 -38.40 190.69 586.21 -4.13 -3.86 -10.61 0.02 371 0.30 0.68 0.76 0.87 0.49 0.30 1.00
67000 -364.32 -40.99 282.51 735.18 -4.24 -4.22 -38.53 0.02 348 0.30 0.68 0.76 0.86 0.50 0.30 1.00
68000 -382.53 -40.71 251.97 880.98 -3.99 -4.12 -15.81 0.02 336 0.30 0.68 0.76 0.86 0.50 0.30 1.00
69000 -382.21 -38.53 204.16 507.18 -3.74 -4.72 -7.78 0.01 391 0.30 0.68 0.76 0.87 0.51 0.30 1.00
70000 -383.41 -36.89 155.45 457.46 -3.67 -4.69 0.80 0.01 352 0.30 0.68 0.76 0.87 0.51 0.30 1.00
71000 -380.85 -42.06 314.00 913.77 -4.19 -4.04 -36.08 0.01 364 0.30 0.68 0.76 0.87 0.51 0.30 1.00
72000 -373.59 -40.62 269.69 691.31 -4.03 -3.64 -6.41 0.02 358 0.30 0.68 0.76 0.87 0.51 0.30 1.00
73000 -379.07 -39.96 237.56 701.38 -3.65 -4.49 -12.05 0.01 320 0.30 0.68 0.75 0.87 0.52 0.30 1.00
74000 -377.66 -43.77 395.69 1065.70 -3.73 -4.53 -4.12 0.01 363 0.30 0.68 0.76 0.87 0.52 0.30 1.00
75000 -370.45 -40.04 242.32 645.39 -3.43 -6.11 -43.30 0.01 347 0.30 0.68 0.76 0.87 0.52 0.30 1.00
76000 -377.27 -47.05 567.97 1690.90 -3.53 -6.41 -63.57 0.01 363 0.30 0.68 0.75 0.87 0.52 0.30 1.00
77000 -366.02 -43.33 374.21 924.31 -3.57 -6.38 -65.51 0.01 342 0.30 0.68 0.75 0.87 0.52 0.30 1.00
78000 -360.47 -43.30 367.16 1058.68 -3.94 -4.72 -32.27 0.01 371 0.30 0.69 0.75 0.87 0.52 0.30 1.00
79000 -381.72 -45.23 478.07 1215.66 -4.00 -4.79 -45.06 0.01 351 0.30 0.68 0.75 0.87 0.52 0.30 1.00
80000 -376.96 -42.57 331.11 1030.76 -4.06 -4.38 -32.17 0.02 313 0.30 0.68 0.75 0.87 0.52 0.30 1.00
81000 -373.18 -43.17 358.77 1081.93 -4.39 -4.14 -44.53 0.02 323 0.30 0.69 0.75 0.87 0.53 0.30 1.00
82000 -373.30 -42.37 327.67 904.87 -4.65 -3.11 -25.25 0.03 332 0.30 0.69 0.76 0.87 0.53 0.30 1.00
83000 -365.89 -44.86 468.57 1138.34 -4.36 -3.66 -17.71 0.02 348 0.30 0.69 0.75 0.87 0.54 0.30 1.00
84000 -373.96 -38.94 200.81 646.39 -4.31 -3.82 -15.32 0.03 327 0.30 0.69 0.75 0.87 0.54 0.30 1.00
85000 -375.05 -45.22 481.19 1249.34 -4.40 -4.01 -40.60 0.02 320 0.30 0.69 0.75 0.87 0.54 0.30 1.00
86000 -370.66 -40.28 254.21 682.40 -4.52 -3.08 -27.38 0.02 348 0.30 0.69 0.75 0.87 0.54 0.30 1.00
87000 -380.16 -44.26 403.60 1332.99 -4.00 -3.99 -9.59 0.02 352 0.30 0.69 0.76 0.87 0.55 0.30 1.00
88000 -372.67 -44.12 413.88 1111.28 -3.95 -4.51 -31.28 0.01 353 0.30 0.69 0.76 0.87 0.55 0.30 1.00
89000 -362.97 -42.44 343.69 860.74 -3.83 -4.30 -19.00 0.02 334 0.30 0.69 0.76 0.87 0.55 0.30 1.00
90000 -364.90 -40.98 274.37 789.07 -4.60 -3.65 -31.37 0.02 349 0.30 0.69 0.75 0.87 0.55 0.30 1.00
91000 -386.33 -37.88 180.07 525.91 -4.36 -3.81 -22.89 0.02 364 0.30 0.68 0.76 0.87 0.55 0.30 1.00
92000 -371.46 -40.02 253.15 612.96 -4.27 -4.28 -39.28 0.02 342 0.30 0.69 0.75 0.87 0.56 0.30 1.00
93000 -376.24 -40.88 278.62 723.87 -4.43 -3.46 -29.90 0.02 360 0.30 0.69 0.76 0.87 0.56 0.30 1.00
94000 -376.88 -41.13 283.18 754.37 -4.82 -2.89 -30.53 0.03 350 0.30 0.68 0.76 0.87 0.56 0.30 1.00
95000 -379.49 -38.59 207.14 517.65 -4.34 -4.05 -30.63 0.02 360 0.30 0.68 0.76 0.87 0.57 0.30 1.00
96000 -378.34 -40.07 247.62 664.86 -4.00 -3.86 -9.18 0.02 347 0.30 0.68 0.76 0.87 0.57 0.30 1.00
97000 -377.78 -40.73 264.90 767.02 -4.42 -3.99 -34.07 0.02 363 0.30 0.68 0.75 0.87 0.57 0.30 1.00
98000 -372.09 -43.61 404.30 943.27 -4.30 -3.93 -31.73 0.02 314 0.30 0.68 0.75 0.87 0.57 0.30 1.00
99000 -382.51 -37.84 172.64 575.76 -4.07 -4.01 -19.07 0.02 378 0.30 0.68 0.75 0.87 0.57 0.30 1.00
100000 -362.49 -40.93 284.79 697.20 -3.71 -4.56 -12.90 0.02 370 0.30 0.68 0.75 0.87 0.57 0.30 1.00 chainEV.sigmoid <- mymcmcEV.sigmoid$load()
chainEV.sigmoid <- set.burnin(chainEV.sigmoid, 0.3)
plot(chainEV.sigmoid)
strees.bayou <- Dtrees.bayou
mymcmcD.sigmoid$run(100000)gen lnL prior alpha sig2 beta_le beta_ri beta_ce beta_sl k .alpha .beta_c .beta_l .beta_r .beta_s .sig2 .theta
1000 -187.80 -20.54 7.28 26.75 -0.99 -5.43 74.58 0.03 352 0.55 0.86 0.91 0.94 0.92 0.44 1.00
2000 -181.37 -20.44 5.93 32.81 -1.43 -4.70 68.02 0.02 402 0.49 0.85 0.92 0.93 0.91 0.44 1.00
3000 -175.73 -21.87 10.60 36.31 -1.71 -4.80 57.79 0.01 379 0.51 0.80 0.91 0.93 0.91 0.43 1.00
4000 -173.96 -22.08 12.31 30.61 -1.45 -5.50 63.03 0.01 412 0.52 0.81 0.90 0.92 0.88 0.42 1.00
5000 -176.10 -23.07 14.29 42.29 -1.10 -5.84 67.11 0.01 406 0.52 0.82 0.89 0.91 0.83 0.42 1.00
6000 -175.99 -22.90 12.23 38.43 -1.04 -6.38 79.77 0.01 370 0.50 0.82 0.88 0.91 0.79 0.41 1.00
7000 -179.85 -22.56 11.58 37.98 -1.38 -6.89 -17.55 0.01 395 0.51 0.83 0.88 0.91 0.76 0.41 1.00
8000 -172.07 -21.85 10.49 30.60 -1.70 -6.41 22.94 0.01 409 0.50 0.84 0.88 0.91 0.74 0.41 1.00
9000 -177.79 -22.77 11.40 35.47 -2.21 -5.91 -22.42 0.01 369 0.50 0.84 0.88 0.91 0.72 0.42 1.00
10000 -169.58 -24.14 16.34 45.72 -2.72 -5.34 -52.81 0.01 369 0.49 0.85 0.88 0.91 0.71 0.42 1.00
11000 -178.01 -21.53 8.10 34.70 -2.93 -5.08 -66.78 0.01 370 0.49 0.85 0.88 0.91 0.71 0.42 1.00
12000 -178.95 -22.94 11.40 48.84 -2.85 -5.12 -74.25 0.01 392 0.49 0.86 0.88 0.91 0.71 0.42 1.00
13000 -182.57 -21.06 8.36 30.95 -3.67 -4.12 -73.31 0.01 391 0.48 0.86 0.88 0.91 0.71 0.41 1.00
14000 -178.21 -23.26 12.84 38.76 -3.23 -4.88 -70.62 0.01 425 0.48 0.86 0.88 0.91 0.72 0.40 1.00
15000 -173.31 -22.27 11.35 33.88 -2.95 -4.95 -70.59 0.01 435 0.48 0.86 0.87 0.92 0.72 0.41 1.00
16000 -173.31 -23.78 15.03 48.36 -2.69 -4.88 -44.70 0.01 379 0.49 0.86 0.87 0.92 0.72 0.40 1.00
17000 -176.70 -23.14 10.78 32.63 -2.86 -5.51 -85.88 0.00 387 0.48 0.86 0.87 0.92 0.71 0.40 1.00
18000 -167.64 -23.86 16.33 46.26 -2.11 -6.19 -38.10 0.01 398 0.48 0.86 0.87 0.92 0.71 0.41 1.00
19000 -177.76 -24.18 14.73 53.16 -2.91 -6.14 -96.11 0.01 381 0.48 0.86 0.87 0.92 0.70 0.40 1.00
20000 -173.50 -21.50 8.60 30.19 -2.85 -5.62 -77.27 0.01 387 0.48 0.86 0.87 0.92 0.70 0.41 1.00
21000 -179.92 -21.30 6.56 26.38 -2.49 -5.33 22.24 0.00 386 0.48 0.86 0.87 0.92 0.69 0.41 1.00
22000 -172.45 -23.21 14.21 39.94 -2.16 -4.86 46.30 0.01 357 0.48 0.86 0.87 0.92 0.69 0.41 1.00
23000 -179.89 -22.62 9.21 34.79 -2.10 -5.61 38.06 0.00 407 0.48 0.86 0.87 0.92 0.69 0.41 1.00
24000 -175.21 -24.95 17.59 65.58 -1.41 -5.89 87.57 0.01 404 0.48 0.86 0.87 0.92 0.69 0.41 1.00
25000 -172.50 -22.92 13.52 32.79 -2.15 -5.65 18.62 0.01 381 0.47 0.86 0.87 0.92 0.69 0.41 1.00
26000 -174.52 -21.02 9.70 26.79 -1.76 -4.70 98.46 0.02 409 0.47 0.86 0.87 0.92 0.69 0.41 1.00
27000 -173.34 -23.32 17.21 40.14 -1.92 -4.48 94.30 0.01 424 0.47 0.86 0.87 0.92 0.69 0.41 1.00
28000 -170.67 -21.89 10.82 36.09 -2.28 -4.23 62.49 0.01 394 0.47 0.86 0.87 0.92 0.69 0.41 1.00
29000 -175.72 -22.14 11.27 36.20 -2.87 -4.26 -12.11 0.01 406 0.47 0.86 0.87 0.92 0.70 0.41 1.00
30000 -176.88 -20.66 6.82 25.70 -3.64 -3.91 -65.24 0.01 402 0.47 0.86 0.87 0.92 0.70 0.41 1.00
31000 -175.26 -24.43 19.43 58.88 -3.72 -3.21 -36.89 0.02 398 0.48 0.86 0.87 0.92 0.71 0.41 1.00
32000 -175.75 -25.71 24.57 72.73 -4.04 -2.38 -41.71 0.01 382 0.48 0.86 0.87 0.92 0.71 0.41 1.00
33000 -179.95 -19.67 6.09 21.05 -4.45 -2.11 -29.14 0.01 402 0.48 0.86 0.87 0.92 0.72 0.41 1.00
34000 -176.69 -21.65 10.08 34.89 -4.24 -2.47 -86.59 0.02 408 0.48 0.86 0.87 0.92 0.72 0.41 1.00
35000 -177.21 -21.73 12.26 28.87 -3.85 -3.33 -65.66 0.02 384 0.48 0.86 0.87 0.92 0.73 0.41 1.00
36000 -176.47 -22.04 12.43 31.92 -3.42 -3.74 -40.22 0.01 398 0.48 0.86 0.87 0.92 0.73 0.41 1.00
37000 -178.10 -20.49 8.00 25.36 -4.23 -3.27 -94.87 0.02 383 0.48 0.86 0.87 0.92 0.73 0.41 1.00
38000 -175.82 -21.80 9.63 37.97 -4.27 -2.94 -33.67 0.03 373 0.48 0.86 0.87 0.92 0.74 0.41 1.00
39000 -182.61 -20.93 9.00 25.25 -3.96 -1.84 5.74 0.03 381 0.47 0.86 0.87 0.92 0.74 0.41 1.00
40000 -177.90 -22.44 12.75 38.82 -4.57 -1.42 8.69 0.02 406 0.48 0.86 0.87 0.92 0.75 0.41 1.00
41000 -178.81 -21.02 7.54 31.99 -3.30 -2.82 36.12 0.01 398 0.48 0.87 0.87 0.92 0.75 0.41 1.00
42000 -169.39 -21.64 10.09 32.44 -3.40 -2.72 21.06 0.03 388 0.48 0.87 0.87 0.92 0.76 0.41 1.00
43000 -179.56 -20.59 7.16 29.67 -4.17 -2.27 -71.33 0.02 386 0.48 0.87 0.87 0.91 0.76 0.41 1.00
44000 -171.05 -24.07 17.71 54.51 -4.07 -2.69 -31.01 0.01 379 0.48 0.87 0.87 0.91 0.77 0.41 1.00
45000 -177.61 -23.22 14.67 43.13 -4.35 -1.69 -13.68 0.01 399 0.48 0.87 0.87 0.91 0.77 0.41 1.00
46000 -179.09 -22.56 12.45 41.55 -3.91 -2.35 -39.00 0.03 420 0.48 0.87 0.87 0.91 0.77 0.41 1.00
47000 -175.31 -22.15 11.93 36.72 -4.33 -2.48 -98.10 0.02 383 0.47 0.87 0.87 0.91 0.78 0.41 1.00
48000 -176.26 -23.26 16.27 38.07 -3.53 -3.12 2.03 0.01 406 0.47 0.87 0.87 0.91 0.78 0.41 1.00
49000 -177.20 -20.78 8.26 26.28 -4.08 -2.91 -53.23 0.01 392 0.47 0.87 0.87 0.92 0.78 0.41 1.00
50000 -174.13 -21.55 9.89 34.04 -3.16 -3.17 2.35 0.02 424 0.47 0.87 0.87 0.92 0.78 0.41 1.00
51000 -180.35 -21.11 7.15 35.58 -3.71 -3.46 -70.67 0.01 403 0.47 0.87 0.87 0.91 0.78 0.41 1.00
52000 -174.74 -22.96 12.60 39.16 -3.18 -4.55 -77.09 0.01 352 0.47 0.87 0.87 0.92 0.79 0.41 1.00
53000 -177.42 -20.85 8.42 26.06 -3.41 -3.40 8.36 0.01 392 0.48 0.87 0.87 0.91 0.79 0.41 1.00
54000 -177.54 -23.17 14.10 49.48 -4.01 -2.41 13.26 0.02 370 0.47 0.87 0.87 0.91 0.79 0.41 1.00
55000 -179.46 -20.77 8.50 26.87 -4.21 -2.79 -71.10 0.02 390 0.47 0.87 0.87 0.91 0.79 0.41 1.00
56000 -172.75 -24.40 21.62 48.76 -4.46 -2.04 -62.51 0.02 406 0.47 0.87 0.87 0.91 0.79 0.41 1.00
57000 -176.01 -28.61 42.50 106.79 -4.34 -2.03 -56.37 0.04 408 0.47 0.87 0.87 0.91 0.80 0.41 1.00
58000 -178.40 -26.55 30.86 72.05 -3.87 -2.22 -0.07 0.04 391 0.48 0.87 0.87 0.91 0.80 0.41 1.00
59000 -169.90 -22.59 13.58 37.54 -3.98 -2.48 -42.59 0.03 360 0.48 0.87 0.87 0.91 0.80 0.41 1.00
60000 -170.88 -24.27 20.41 49.33 -3.84 -2.84 -19.00 0.01 373 0.47 0.87 0.87 0.91 0.80 0.41 1.00
61000 -168.31 -23.34 18.05 38.30 -3.92 -2.72 -24.42 0.02 398 0.47 0.87 0.87 0.91 0.80 0.41 1.00
62000 -172.46 -26.22 28.38 79.84 -3.74 -3.36 -65.67 0.02 395 0.47 0.87 0.87 0.91 0.81 0.41 1.00
63000 -175.80 -22.50 11.70 38.47 -3.60 -3.62 -32.84 0.01 403 0.47 0.87 0.87 0.91 0.81 0.41 1.00
64000 -179.54 -23.08 14.37 42.07 -3.95 -2.80 -41.80 0.01 391 0.47 0.87 0.87 0.91 0.81 0.41 1.00
65000 -174.25 -21.92 10.74 37.27 -3.95 -2.79 -47.47 0.02 437 0.47 0.87 0.87 0.91 0.81 0.41 1.00
66000 -179.34 -20.14 5.75 24.07 -4.11 -2.41 -22.33 0.04 396 0.47 0.87 0.87 0.91 0.81 0.41 1.00
67000 -177.59 -25.23 20.62 57.23 -4.73 -2.18 -96.67 0.06 368 0.47 0.87 0.87 0.91 0.81 0.41 1.00
68000 -176.14 -24.10 16.85 37.94 -3.73 -2.00 19.81 0.07 388 0.47 0.87 0.87 0.91 0.82 0.41 1.00
69000 -172.37 -23.90 14.63 36.46 -4.11 -1.78 -17.61 0.09 360 0.47 0.87 0.87 0.91 0.82 0.41 1.00
70000 -179.17 -22.68 8.17 32.60 -4.06 -1.90 -35.89 0.11 386 0.47 0.87 0.87 0.91 0.82 0.41 1.00
71000 -174.69 -23.88 10.93 37.90 -4.76 -1.70 -92.56 0.13 415 0.47 0.87 0.87 0.91 0.82 0.41 1.00
72000 -174.99 -26.34 18.23 47.51 -4.46 -2.12 -77.35 0.17 384 0.47 0.87 0.87 0.91 0.83 0.41 1.00
73000 -179.07 -23.37 8.29 35.11 -3.90 -2.05 16.34 0.15 408 0.47 0.87 0.87 0.91 0.83 0.41 1.00
74000 -179.74 -23.26 7.56 34.47 -3.86 -1.84 84.80 0.15 415 0.47 0.87 0.87 0.91 0.83 0.41 1.00
75000 -176.60 -24.86 12.68 46.87 -4.24 -1.62 76.95 0.14 388 0.47 0.87 0.87 0.91 0.83 0.41 1.00
76000 -179.16 -24.79 12.21 44.33 -4.21 -1.79 59.65 0.15 420 0.47 0.87 0.87 0.91 0.83 0.41 1.00
77000 -176.77 -23.86 10.04 38.97 -4.94 -1.53 -89.77 0.14 372 0.47 0.87 0.87 0.91 0.84 0.41 1.00
78000 -177.98 -24.39 11.36 37.32 -4.86 -1.84 -85.18 0.16 425 0.47 0.87 0.87 0.91 0.84 0.41 1.00
79000 -179.28 -25.62 13.57 51.23 -4.82 -1.27 -41.03 0.17 386 0.47 0.87 0.87 0.91 0.84 0.41 1.00
80000 -169.34 -25.05 16.70 41.51 -4.44 -1.47 -33.89 0.12 406 0.47 0.87 0.87 0.91 0.84 0.41 1.00
81000 -174.14 -24.97 14.75 39.06 -4.64 -2.31 -90.69 0.15 383 0.47 0.87 0.87 0.91 0.84 0.41 1.00
82000 -173.97 -25.19 16.43 40.99 -4.64 -1.43 -75.79 0.13 404 0.47 0.87 0.87 0.91 0.85 0.41 1.00
83000 -178.64 -25.36 16.87 45.27 -5.03 -1.24 -35.80 0.13 381 0.47 0.87 0.87 0.91 0.85 0.41 1.00
84000 -181.50 -21.63 7.54 23.15 -4.93 -1.29 -55.76 0.10 368 0.47 0.87 0.87 0.91 0.85 0.41 1.00
85000 -178.57 -22.21 7.23 29.82 -4.90 -1.03 16.26 0.11 411 0.47 0.87 0.87 0.91 0.85 0.41 1.00
86000 -180.93 -23.05 9.74 32.46 -4.96 -0.71 -95.12 0.11 392 0.47 0.87 0.87 0.91 0.85 0.41 1.00
87000 -177.47 -22.23 8.45 29.48 -4.83 -1.24 -27.34 0.09 366 0.47 0.88 0.87 0.91 0.85 0.41 1.00
88000 -179.32 -22.10 9.34 28.33 -3.90 -2.42 2.58 0.08 437 0.47 0.88 0.87 0.91 0.85 0.41 1.00
89000 -179.70 -21.91 7.94 35.35 -5.06 -1.78 -86.55 0.06 399 0.47 0.88 0.87 0.91 0.86 0.41 1.00
90000 -178.87 -21.19 6.80 23.69 -5.14 -1.17 -59.60 0.08 400 0.47 0.88 0.87 0.91 0.86 0.41 1.00
91000 -179.25 -21.67 7.50 27.80 -4.57 -1.43 -61.86 0.08 387 0.47 0.88 0.87 0.91 0.86 0.41 1.00
92000 -178.81 -22.12 6.35 30.40 -5.25 -1.35 -54.38 0.12 390 0.47 0.88 0.87 0.91 0.86 0.41 1.00
93000 -175.21 -24.36 12.69 36.54 -4.14 -1.87 -10.89 0.14 392 0.47 0.88 0.87 0.91 0.86 0.41 1.00
94000 -176.99 -24.51 11.50 47.70 -4.27 -1.95 -20.92 0.13 398 0.47 0.88 0.87 0.91 0.86 0.41 1.00
95000 -177.31 -23.96 9.96 38.29 -4.49 -1.83 21.87 0.15 372 0.47 0.88 0.87 0.91 0.86 0.41 1.00
96000 -174.08 -26.88 20.24 60.30 -4.21 -1.98 17.57 0.16 366 0.47 0.88 0.87 0.91 0.87 0.41 1.00
97000 -178.95 -23.97 9.82 33.18 -3.89 -1.81 2.44 0.17 386 0.47 0.88 0.87 0.91 0.87 0.41 1.00
98000 -179.14 -24.44 9.68 38.19 -4.91 -1.62 -79.79 0.19 403 0.47 0.88 0.87 0.91 0.87 0.41 1.00
99000 -178.56 -25.81 13.96 52.65 -4.69 -1.55 -70.47 0.17 366 0.47 0.88 0.87 0.91 0.87 0.41 1.00
100000 -172.91 -23.74 9.55 30.21 -4.19 -2.49 -81.92 0.17 375 0.47 0.88 0.87 0.91 0.87 0.41 1.00 chainD.sigmoid <- mymcmcD.sigmoid$load()
chainD.sigmoid <- set.burnin(chainD.sigmoid, 0.3)
plot(chainD.sigmoid)
strees.bayou <- EVtrees.bayou
mymcmcEV.linear$run(100000)gen lnL prior alpha sig2 beta_in beta_sl k .alpha .beta_i .beta_s .sig2 .theta
1000 -375.69 -32.60 257.03 687.85 -5.79 0.01 307 0.41 0.63 0.67 0.35 1.00
2000 -366.95 -32.65 259.55 693.37 -6.00 0.01 375 0.37 0.55 0.60 0.35 1.00
3000 -370.39 -35.98 384.68 1173.67 -5.85 0.01 348 0.35 0.54 0.58 0.30 1.00
4000 -379.18 -33.03 263.89 797.83 -6.00 0.01 353 0.34 0.52 0.55 0.29 1.00
5000 -369.36 -34.32 314.83 922.01 -5.84 0.01 347 0.33 0.51 0.55 0.29 1.00
6000 -377.09 -31.86 235.75 604.47 -5.83 0.01 323 0.33 0.51 0.55 0.30 1.00
7000 -366.39 -37.61 485.26 1313.04 -5.86 0.01 325 0.32 0.51 0.55 0.30 1.00
8000 -381.82 -31.29 208.55 635.44 -5.99 0.01 367 0.31 0.50 0.55 0.30 1.00
9000 -378.95 -29.93 174.32 514.12 -5.87 0.01 323 0.31 0.50 0.55 0.30 1.00
10000 -370.47 -34.77 333.27 980.12 -5.87 0.01 348 0.31 0.50 0.55 0.31 1.00
11000 -368.83 -35.64 387.70 969.08 -6.01 0.01 349 0.30 0.50 0.54 0.31 1.00
12000 -380.82 -30.08 179.91 512.32 -5.98 0.01 347 0.30 0.50 0.54 0.30 1.00
13000 -381.53 -33.43 276.08 857.68 -5.97 0.01 367 0.30 0.50 0.54 0.31 1.00
14000 -373.77 -35.85 395.21 1019.63 -5.92 0.01 357 0.30 0.49 0.54 0.31 1.00
15000 -371.95 -30.93 206.70 541.97 -5.84 0.01 368 0.30 0.49 0.54 0.30 1.00
16000 -383.06 -32.76 257.61 746.48 -5.88 0.01 351 0.30 0.49 0.54 0.30 1.00
17000 -375.04 -34.95 333.10 1073.25 -5.81 0.01 323 0.30 0.49 0.54 0.30 1.00
18000 -377.12 -30.51 188.54 559.75 -5.85 0.01 369 0.31 0.49 0.53 0.30 1.00
19000 -369.89 -37.44 462.38 1396.61 -5.83 0.01 332 0.30 0.49 0.53 0.30 1.00
20000 -382.44 -28.34 140.40 402.00 -6.02 0.01 358 0.30 0.49 0.53 0.30 1.00
21000 -379.90 -31.46 221.08 592.00 -5.85 0.01 378 0.30 0.49 0.53 0.30 1.00
22000 -376.02 -31.30 218.71 560.95 -5.89 0.01 360 0.30 0.48 0.53 0.30 1.00
23000 -374.49 -36.92 441.28 1243.68 -6.04 0.01 338 0.30 0.49 0.53 0.30 1.00
24000 -375.98 -34.67 335.43 911.70 -5.84 0.01 367 0.30 0.49 0.53 0.30 1.00
25000 -372.27 -30.53 182.06 621.86 -5.96 0.01 332 0.30 0.49 0.53 0.30 1.00
26000 -368.04 -33.34 282.10 770.80 -5.88 0.01 314 0.30 0.49 0.53 0.30 1.00
27000 -368.68 -39.44 609.56 1595.81 -5.80 0.01 332 0.30 0.49 0.53 0.30 1.00
28000 -366.47 -33.90 301.38 847.67 -5.87 0.01 347 0.30 0.49 0.52 0.30 1.00
29000 -373.25 -37.09 451.38 1263.30 -5.94 0.01 357 0.30 0.49 0.52 0.30 1.00
30000 -389.32 -31.05 198.31 643.29 -5.92 0.01 310 0.30 0.49 0.52 0.30 1.00
31000 -362.78 -35.10 348.89 1011.10 -5.94 0.01 311 0.30 0.49 0.52 0.30 1.00
32000 -368.14 -33.12 287.81 651.10 -5.81 0.01 334 0.30 0.49 0.52 0.30 1.00
33000 -379.40 -33.66 275.14 972.39 -5.91 0.01 347 0.30 0.49 0.52 0.30 1.00
34000 -374.01 -32.37 236.45 777.70 -5.88 0.01 368 0.30 0.49 0.52 0.30 1.00
35000 -371.21 -31.57 219.12 639.09 -5.87 0.01 330 0.30 0.49 0.52 0.30 1.00
36000 -380.42 -34.11 295.82 995.19 -5.96 0.01 375 0.30 0.49 0.52 0.30 1.00
37000 -375.00 -31.24 204.49 652.18 -5.90 0.01 347 0.30 0.49 0.52 0.30 1.00
38000 -374.40 -32.50 265.40 599.71 -5.77 0.01 368 0.30 0.49 0.52 0.30 1.00
39000 -382.89 -29.01 145.64 511.42 -5.85 0.01 357 0.30 0.49 0.52 0.30 1.00
40000 -367.62 -35.21 362.79 951.39 -6.05 0.01 342 0.30 0.48 0.52 0.30 1.00
41000 -382.53 -31.72 220.40 675.94 -5.72 0.01 336 0.30 0.48 0.52 0.30 1.00
42000 -382.23 -31.78 225.07 662.61 -6.09 0.01 336 0.30 0.48 0.53 0.30 1.00
43000 -377.17 -33.00 273.90 704.71 -5.82 0.01 357 0.30 0.49 0.53 0.30 1.00
44000 -375.87 -29.80 172.61 495.82 -5.88 0.01 350 0.30 0.48 0.53 0.30 1.00
45000 -374.75 -32.52 256.73 666.34 -5.87 0.01 350 0.30 0.48 0.53 0.30 1.00
46000 -373.71 -34.25 328.44 787.57 -6.07 0.01 330 0.30 0.48 0.53 0.30 1.00
47000 -374.73 -35.95 383.05 1171.61 -5.99 0.01 350 0.30 0.48 0.53 0.30 1.00
48000 -367.66 -36.22 401.77 1159.16 -5.78 0.01 348 0.30 0.48 0.53 0.30 1.00
49000 -369.72 -33.54 294.28 754.66 -5.87 0.01 347 0.30 0.48 0.53 0.30 1.00
50000 -382.87 -32.50 249.68 711.46 -5.87 0.01 363 0.30 0.48 0.53 0.30 1.00
51000 -384.55 -34.71 339.83 898.56 -5.89 0.01 313 0.30 0.48 0.53 0.30 1.00
52000 -378.58 -30.57 186.96 592.08 -5.90 0.01 314 0.30 0.48 0.53 0.30 1.00
53000 -379.79 -30.89 197.31 600.22 -5.88 0.01 314 0.30 0.48 0.53 0.30 1.00
54000 -374.31 -33.60 280.72 891.50 -6.00 0.01 358 0.30 0.48 0.53 0.30 1.00
55000 -384.84 -34.71 333.45 948.97 -5.93 0.01 333 0.30 0.48 0.53 0.30 1.00
56000 -371.33 -31.87 232.07 636.28 -5.83 0.01 347 0.30 0.48 0.53 0.30 1.00
57000 -378.38 -30.27 184.82 520.77 -5.75 0.01 327 0.30 0.48 0.53 0.30 1.00
58000 -375.30 -30.05 172.49 561.55 -6.06 0.01 325 0.30 0.48 0.53 0.30 1.00
59000 -366.62 -38.39 519.00 1570.93 -5.81 0.01 365 0.30 0.48 0.53 0.30 1.00
60000 -367.25 -35.17 366.86 900.51 -5.91 0.01 348 0.30 0.48 0.53 0.30 1.00
61000 -380.20 -36.12 389.95 1198.36 -5.72 0.01 353 0.30 0.48 0.53 0.30 1.00
62000 -377.69 -34.17 314.07 861.45 -5.96 0.01 353 0.30 0.48 0.53 0.30 1.00
63000 -380.99 -35.02 348.89 967.98 -5.79 0.01 326 0.30 0.48 0.53 0.30 1.00
64000 -373.84 -33.48 281.60 828.20 -5.74 0.01 329 0.30 0.48 0.53 0.30 1.00
65000 -372.40 -38.43 540.16 1418.66 -5.86 0.01 364 0.30 0.48 0.53 0.30 1.00
66000 -384.31 -31.04 199.78 625.14 -5.73 0.01 350 0.30 0.48 0.53 0.30 1.00
67000 -375.68 -32.03 229.40 712.58 -5.97 0.01 348 0.30 0.48 0.53 0.30 1.00
68000 -379.97 -32.51 241.54 786.40 -5.92 0.01 347 0.30 0.48 0.53 0.30 1.00
69000 -374.66 -34.73 332.71 961.36 -5.79 0.01 314 0.30 0.48 0.53 0.30 1.00
70000 -379.11 -36.26 399.58 1208.48 -5.92 0.01 367 0.30 0.48 0.53 0.30 1.00
71000 -373.87 -33.84 300.11 831.56 -5.95 0.01 358 0.30 0.48 0.53 0.30 1.00
72000 -384.95 -31.35 211.75 628.34 -5.84 0.01 342 0.30 0.48 0.53 0.30 1.00
73000 -377.22 -32.50 248.54 723.25 -5.89 0.01 357 0.30 0.48 0.53 0.30 1.00
74000 -378.22 -33.14 280.06 715.08 -5.91 0.01 378 0.30 0.48 0.53 0.30 1.00
75000 -367.93 -36.18 391.33 1238.00 -5.91 0.01 346 0.30 0.48 0.53 0.30 1.00
76000 -386.79 -32.04 221.68 779.72 -5.78 0.01 336 0.30 0.48 0.53 0.30 1.00
77000 -370.74 -31.18 219.70 522.45 -5.86 0.01 348 0.30 0.48 0.53 0.30 1.00
78000 -366.94 -42.26 863.07 2087.46 -5.88 0.01 314 0.30 0.48 0.53 0.30 1.00
79000 -380.22 -36.06 405.41 1046.60 -5.84 0.01 320 0.30 0.48 0.53 0.30 1.00
80000 -374.16 -32.58 253.40 709.34 -5.77 0.01 324 0.30 0.48 0.53 0.30 1.00
81000 -381.50 -34.18 320.51 817.15 -5.92 0.01 336 0.30 0.48 0.53 0.30 1.00
82000 -383.02 -31.47 209.96 683.45 -6.05 0.01 367 0.30 0.48 0.53 0.30 1.00
83000 -373.13 -33.12 268.86 792.02 -5.92 0.01 376 0.30 0.48 0.53 0.30 1.00
84000 -376.62 -31.29 209.92 623.25 -5.88 0.01 369 0.30 0.48 0.53 0.30 1.00
85000 -372.13 -30.72 202.01 521.35 -5.97 0.01 349 0.30 0.48 0.53 0.30 1.00
86000 -374.03 -34.24 330.26 771.61 -5.96 0.01 317 0.30 0.48 0.53 0.30 1.00
87000 -375.82 -33.25 278.00 766.59 -5.77 0.01 314 0.30 0.48 0.53 0.30 1.00
88000 -376.60 -32.75 259.24 729.83 -5.89 0.01 361 0.30 0.48 0.53 0.30 1.00
89000 -380.50 -32.88 260.44 767.08 -5.95 0.01 348 0.30 0.48 0.53 0.30 1.00
90000 -386.14 -30.97 210.42 528.42 -5.84 0.01 367 0.30 0.48 0.53 0.30 1.00
91000 -385.05 -34.13 312.48 856.09 -5.93 0.01 336 0.30 0.48 0.53 0.30 1.00
92000 -387.15 -29.50 164.41 481.90 -5.84 0.01 348 0.30 0.48 0.53 0.30 1.00
93000 -374.98 -36.95 435.42 1316.14 -5.91 0.01 369 0.30 0.48 0.53 0.30 1.00
94000 -382.91 -30.54 190.69 551.82 -5.97 0.01 375 0.30 0.48 0.53 0.30 1.00
95000 -370.75 -30.28 186.87 511.70 -5.95 0.01 348 0.30 0.48 0.53 0.30 1.00
96000 -376.11 -33.27 264.92 888.97 -6.11 0.01 351 0.30 0.48 0.53 0.30 1.00
97000 -381.73 -31.46 223.78 572.28 -5.94 0.01 378 0.30 0.48 0.53 0.30 1.00
98000 -383.43 -34.52 326.61 916.56 -5.96 0.01 321 0.30 0.48 0.53 0.30 1.00
99000 -373.88 -35.73 380.98 1065.74 -5.88 0.01 314 0.30 0.48 0.53 0.30 1.00
100000 -376.88 -31.83 233.21 615.43 -5.89 0.01 350 0.30 0.48 0.53 0.30 1.00 chainEV.linear <- mymcmcEV.linear$load()
chainEV.linear <- set.burnin(chainEV.linear, 0.3)
plot(chainEV.linear)
strees.bayou <- Dtrees.bayou
mymcmcD.linear$run(100000)gen lnL prior alpha sig2 beta_in beta_sl k .alpha .beta_i .beta_s .sig2 .theta
1000 -174.14 -14.71 15.34 29.65 -5.38 0.01 388 0.50 0.73 0.65 0.44 1.00
2000 -173.79 -15.04 12.30 42.92 -4.93 0.01 435 0.46 0.70 0.63 0.40 1.00
3000 -172.51 -14.35 11.52 34.19 -5.09 0.01 372 0.45 0.70 0.65 0.39 1.00
4000 -171.20 -14.95 13.76 36.00 -4.99 0.01 424 0.45 0.69 0.64 0.38 1.00
5000 -176.05 -15.29 13.41 43.56 -4.93 0.01 385 0.46 0.68 0.63 0.37 1.00
6000 -178.46 -12.94 6.76 25.68 -4.51 0.01 406 0.47 0.68 0.63 0.37 1.00
7000 -175.65 -14.74 12.02 41.38 -5.38 0.01 380 0.46 0.68 0.64 0.38 1.00
8000 -175.56 -13.69 10.04 28.28 -4.98 0.01 437 0.46 0.68 0.64 0.38 1.00
9000 -178.13 -14.42 10.25 41.08 -5.15 0.01 381 0.45 0.68 0.64 0.38 1.00
10000 -173.51 -14.37 11.57 34.91 -5.20 0.01 369 0.45 0.69 0.65 0.39 1.00
11000 -172.69 -14.83 12.70 37.46 -4.98 0.01 402 0.45 0.69 0.65 0.39 1.00
12000 -174.88 -15.28 13.12 45.92 -5.06 0.01 380 0.45 0.69 0.66 0.39 1.00
13000 -175.76 -14.94 13.38 36.81 -4.94 0.01 372 0.45 0.69 0.66 0.39 1.00
14000 -177.03 -14.01 10.74 29.81 -4.87 0.01 383 0.45 0.68 0.66 0.39 1.00
15000 -172.72 -19.82 34.32 103.64 -5.11 0.01 412 0.46 0.68 0.66 0.39 1.00
16000 -181.03 -15.23 14.56 36.86 -4.83 0.01 399 0.46 0.68 0.66 0.39 1.00
17000 -178.67 -16.31 16.12 58.66 -5.09 0.01 366 0.46 0.69 0.65 0.39 1.00
18000 -179.81 -12.82 6.30 24.16 -4.31 0.01 404 0.46 0.69 0.66 0.40 1.00
19000 -175.07 -15.46 15.35 39.91 -4.97 0.01 379 0.46 0.69 0.66 0.40 1.00
20000 -177.94 -13.81 9.71 32.18 -5.13 0.01 369 0.46 0.69 0.66 0.40 1.00
21000 -175.49 -15.10 13.64 39.73 -5.05 0.01 396 0.46 0.69 0.66 0.40 1.00
22000 -166.27 -17.32 24.68 52.39 -5.20 0.01 388 0.46 0.69 0.66 0.39 1.00
23000 -182.43 -17.01 18.43 63.12 -4.73 0.01 391 0.46 0.69 0.66 0.40 1.00
24000 -171.77 -18.86 33.63 67.93 -5.25 0.01 391 0.46 0.69 0.66 0.40 1.00
25000 -177.61 -14.75 11.03 44.84 -5.20 0.01 387 0.46 0.69 0.66 0.40 1.00
26000 -176.94 -12.73 7.85 23.36 -5.08 0.01 398 0.46 0.69 0.66 0.40 1.00
27000 -174.22 -18.00 25.87 67.89 -5.16 0.01 380 0.46 0.69 0.66 0.40 1.00
28000 -175.38 -14.81 12.59 38.35 -5.06 0.01 395 0.46 0.69 0.66 0.40 1.00
29000 -179.72 -15.68 15.08 49.16 -5.33 0.00 407 0.46 0.69 0.66 0.40 1.00
30000 -176.36 -14.80 12.18 41.26 -5.27 0.01 409 0.46 0.69 0.66 0.40 1.00
31000 -172.62 -15.33 13.78 45.17 -5.18 0.01 404 0.46 0.69 0.66 0.40 1.00
32000 -171.11 -15.47 15.33 41.58 -5.12 0.01 366 0.46 0.69 0.66 0.40 1.00
33000 -174.75 -14.26 9.72 38.54 -4.93 0.01 402 0.46 0.69 0.66 0.40 1.00
34000 -175.51 -20.72 39.62 117.07 -4.82 0.01 387 0.46 0.69 0.66 0.40 1.00
35000 -178.46 -12.84 7.76 25.57 -5.21 0.01 370 0.47 0.69 0.66 0.40 1.00
36000 -175.46 -13.60 8.00 34.72 -5.00 0.01 372 0.47 0.69 0.66 0.40 1.00
37000 -174.86 -15.11 12.61 45.05 -5.14 0.01 396 0.47 0.69 0.66 0.40 1.00
38000 -179.89 -12.95 7.76 27.74 -5.38 0.00 403 0.47 0.69 0.66 0.40 1.00
39000 -177.68 -13.89 9.53 31.47 -4.78 0.01 387 0.47 0.69 0.66 0.40 1.00
40000 -179.80 -13.18 9.28 23.82 -4.95 0.01 374 0.47 0.69 0.66 0.40 1.00
41000 -175.59 -14.32 11.19 32.27 -4.77 0.01 404 0.46 0.69 0.66 0.40 1.00
42000 -177.91 -18.77 30.69 77.14 -5.34 0.01 392 0.46 0.69 0.66 0.40 1.00
43000 -176.86 -13.94 9.31 34.64 -4.96 0.01 383 0.46 0.69 0.66 0.40 1.00
44000 -178.40 -15.13 13.53 43.31 -5.39 0.01 399 0.46 0.69 0.66 0.40 1.00
45000 -168.49 -15.13 14.34 37.01 -4.96 0.01 366 0.46 0.69 0.66 0.40 1.00
46000 -174.16 -16.95 20.30 58.24 -5.12 0.01 368 0.46 0.69 0.66 0.40 1.00
47000 -175.05 -15.31 14.18 43.73 -5.27 0.01 406 0.46 0.69 0.66 0.40 1.00
48000 -175.41 -16.64 18.63 54.79 -4.95 0.01 415 0.46 0.69 0.66 0.40 1.00
49000 -175.81 -14.03 10.14 34.81 -5.24 0.01 415 0.46 0.69 0.66 0.40 1.00
50000 -177.80 -13.23 7.41 32.29 -5.15 0.01 408 0.46 0.69 0.66 0.40 1.00
51000 -174.49 -14.98 13.61 37.65 -5.04 0.01 437 0.46 0.69 0.66 0.40 1.00
52000 -176.22 -14.71 11.91 39.92 -5.17 0.01 387 0.46 0.69 0.66 0.40 1.00
53000 -173.55 -14.42 12.43 32.53 -5.14 0.01 403 0.46 0.69 0.66 0.40 1.00
54000 -178.08 -16.07 18.76 41.57 -5.06 0.01 372 0.46 0.69 0.66 0.40 1.00
55000 -176.59 -13.84 9.26 35.08 -5.24 0.01 411 0.46 0.69 0.66 0.40 1.00
56000 -171.58 -17.88 24.61 66.40 -4.95 0.01 372 0.46 0.69 0.66 0.40 1.00
57000 -174.79 -14.48 10.46 35.49 -4.55 0.01 408 0.46 0.69 0.66 0.40 1.00
58000 -180.79 -14.02 9.07 38.49 -5.11 0.01 407 0.46 0.69 0.66 0.40 1.00
59000 -178.81 -14.82 12.12 39.86 -5.01 0.01 369 0.46 0.69 0.66 0.40 1.00
60000 -173.84 -14.19 9.79 36.69 -4.91 0.01 399 0.47 0.69 0.66 0.40 1.00
61000 -176.48 -15.29 13.10 48.30 -5.29 0.01 374 0.47 0.69 0.66 0.40 1.00
62000 -176.46 -16.18 16.75 53.07 -5.18 0.01 385 0.47 0.69 0.66 0.40 1.00
63000 -173.85 -15.69 14.10 46.71 -4.69 0.01 380 0.47 0.69 0.66 0.40 1.00
64000 -169.73 -15.74 17.02 39.74 -4.96 0.01 388 0.47 0.69 0.66 0.40 1.00
65000 -177.04 -15.29 12.69 44.76 -4.77 0.01 393 0.47 0.69 0.66 0.40 1.00
66000 -177.82 -15.95 17.01 47.05 -5.26 0.01 381 0.46 0.69 0.66 0.40 1.00
67000 -179.80 -14.46 11.31 37.61 -5.19 0.00 426 0.47 0.69 0.66 0.40 1.00
68000 -178.54 -14.49 11.53 37.63 -5.24 0.00 423 0.47 0.69 0.66 0.40 1.00
69000 -173.12 -15.14 14.39 36.58 -4.90 0.01 379 0.47 0.69 0.66 0.40 1.00
70000 -176.81 -15.27 13.30 45.06 -5.08 0.01 399 0.47 0.69 0.66 0.40 1.00
71000 -169.43 -16.99 20.11 56.87 -4.86 0.01 383 0.46 0.69 0.66 0.40 1.00
72000 -178.33 -17.24 20.58 64.82 -5.03 0.01 423 0.46 0.69 0.65 0.40 1.00
73000 -176.30 -13.17 8.31 27.26 -5.03 0.01 395 0.47 0.69 0.65 0.40 1.00
74000 -175.09 -18.53 28.39 73.29 -4.99 0.01 368 0.46 0.69 0.65 0.40 1.00
75000 -169.79 -16.89 21.07 50.26 -4.85 0.01 423 0.46 0.69 0.65 0.40 1.00
76000 -175.25 -14.14 9.93 36.13 -5.01 0.01 352 0.46 0.69 0.65 0.40 1.00
77000 -171.12 -16.08 18.38 41.30 -4.86 0.01 395 0.46 0.69 0.65 0.40 1.00
78000 -174.80 -13.35 9.13 26.07 -4.91 0.01 404 0.46 0.69 0.65 0.40 1.00
79000 -179.75 -13.30 8.21 31.52 -5.47 0.01 404 0.46 0.69 0.65 0.40 1.00
80000 -172.72 -17.58 23.02 61.27 -4.82 0.01 372 0.47 0.69 0.65 0.40 1.00
81000 -173.91 -15.31 15.78 32.93 -4.67 0.01 388 0.47 0.69 0.65 0.40 1.00
82000 -172.69 -15.66 14.91 43.81 -4.78 0.01 395 0.47 0.69 0.65 0.40 1.00
83000 -175.70 -15.96 15.35 53.87 -5.19 0.01 420 0.47 0.69 0.65 0.40 1.00
84000 -171.35 -15.44 14.90 43.00 -5.16 0.01 408 0.47 0.69 0.65 0.40 1.00
85000 -173.64 -15.67 14.90 46.30 -4.97 0.01 396 0.47 0.69 0.65 0.40 1.00
86000 -178.07 -15.79 15.28 50.06 -5.24 0.01 411 0.47 0.69 0.65 0.40 1.00
87000 -180.07 -12.44 6.66 23.81 -5.09 0.01 399 0.47 0.69 0.65 0.40 1.00
88000 -174.67 -18.98 26.84 98.73 -4.91 0.01 415 0.47 0.69 0.65 0.40 1.00
89000 -176.27 -15.24 14.38 38.91 -4.95 0.01 385 0.47 0.69 0.65 0.40 1.00
90000 -174.45 -16.36 17.52 52.90 -5.03 0.01 391 0.47 0.69 0.65 0.40 1.00
91000 -171.66 -16.12 17.17 45.22 -4.78 0.01 381 0.47 0.68 0.65 0.40 1.00
92000 -172.61 -13.25 8.22 28.63 -5.05 0.01 390 0.47 0.68 0.65 0.40 1.00
93000 -184.38 -12.07 4.54 23.08 -4.45 0.01 391 0.47 0.68 0.65 0.40 1.00
94000 -176.00 -13.45 7.99 33.05 -5.12 0.01 404 0.47 0.68 0.65 0.41 1.00
95000 -178.27 -14.75 11.77 42.98 -5.43 0.01 391 0.47 0.69 0.65 0.41 1.00
96000 -169.50 -14.74 11.60 38.79 -4.84 0.01 394 0.47 0.69 0.65 0.41 1.00
97000 -176.12 -15.31 13.79 42.61 -4.95 0.01 392 0.46 0.68 0.65 0.41 1.00
98000 -167.91 -15.73 16.69 38.65 -4.76 0.01 366 0.47 0.68 0.65 0.40 1.00
99000 -176.85 -15.34 12.79 45.61 -4.77 0.01 406 0.47 0.68 0.65 0.40 1.00
100000 -174.30 -13.25 8.37 28.71 -5.14 0.01 382 0.47 0.68 0.65 0.40 1.00 chainD.linear <- mymcmcD.linear$load()
chainD.linear <- set.burnin(chainD.linear, 0.3)
plot(chainD.linear)
optimizeModelsOverSimmap <- function(i, strees.bayou=strees.bayou, chain.sigmoid=chain.sigmoid, chain.linear=chain.linear, chain.step=chain.step, cache = cache){
sb <- strees.bayou[[i]]$sb
t2 <- strees.bayou[[i]]$t2
loc <- strees.bayou[[i]]$loc
k <- length(sb)
ntheta <- ncat - 1
lik.sigmoid <- function(pp){
pars <- list(alpha = pp[1],
sig2 = pp[2],
beta_left = pp[3],
beta_right = pp[4],
beta_center = pp[5],
beta_slope = pp[6],
k = k,
ntheta=ntheta,
theta = pp[3] + pp[4]/(1 + exp(pp[6] * (midbins - pp[5]))),
sb = sb,
loc=loc,
t2=t2)
model.sigmoidTheta$lik.fn(pars, cache, cache$dat)$loglik[,1]
}
lik.sigmoid0 <- function(pp){
pars <- list(alpha = pp[1],
sig2 = pp[2],
beta_left = pp[3],
beta_right = pp[4],
beta_center = 0,
beta_slope = pp[5],
k = k,
ntheta=ntheta,
theta = pp[3] + pp[4]/(1 + exp(pp[5] * (midbins - 0))),
sb = sb,
loc=loc,
t2=t2)
model.sigmoidTheta$lik.fn(pars, cache, cache$dat)$loglik[,1]
}
lik.tanhLin <- function(pp){
pars <- list(alpha = pp[1],
sig2 = pp[2],
beta_left = pp[3],
beta_right = pp[4],
beta_center = pp[5],
beta_slope = pp[6],
k = k,
ntheta=ntheta,
theta = pp[3] + pp[6]*midbins + pp[4]*tanh(midbins - pp[5]),
sb = sb,
loc=loc,
t2=t2)
model.sigmoidTheta$lik.fn(pars, cache, cache$dat)$loglik[,1]
}
## Parameter order: alpha, sig2, beta_left, beta_right, beta_center, beta_slope
pars.sigmoid <- pull.pars(length(chain.sigmoid$gen), chain.sigmoid, model.sigmoidTheta)
pp.sigmoid <- unname(c(pars.sigmoid$alpha, pars.sigmoid$sig2, pars.sigmoid$beta_left, pars.sigmoid$beta_right, pars.sigmoid$beta_center, pars.sigmoid$beta_slope))
pp.sigmoid0 <- pp.sigmoid[-5]
lik.step <- function(pp){
pars <- list(alpha = pp[1],
sig2 = pp[2],
beta_left = pp[3],
beta_right = pp[4],
beta_center = pp[5],
k = k,
ntheta=ntheta,
theta = ifelse(midbins>pp[5], pp[4], pp[3]),
sb = sb,
loc=loc,
t2=t2)
model.stepTheta$lik.fn(pars, cache, cache$dat)$loglik[,1]
}
lik.step0 <- function(pp){
pars <- list(alpha = pp[1],
sig2 = pp[2],
beta_left = pp[3],
beta_right = pp[4],
beta_center = 0,
k = k,
ntheta=ntheta,
theta = ifelse(midbins>0, pp[4], pp[3]),
sb = sb,
loc=loc,
t2=t2)
model.stepTheta$lik.fn(pars, cache, cache$dat)$loglik[,1]
}
## Parameter order: alpha, sig2, beta_left, beta_right, beta_center, beta_slope
pars.step <- pull.pars(length(chain.step$gen), chain.step, model.stepTheta)
pp.step <- unname(c(pars.step$alpha, pars.step$sig2, pars.step$beta_left, pars.step$beta_right, pars.step$beta_center))
pp.step0 <- pp.step[-5]
lik.linear <- function(pp){
pars <- list(alpha = pp[1],
sig2 = pp[2],
beta_intercept = pp[3],
beta_slope = pp[4],
k = k,
ntheta=ntheta,
theta = pp[3] + pp[4]*midbins,
sb = sb,
loc=loc,
t2=t2)
model.linearTheta$lik.fn(pars, cache, cache$dat)$loglik[,1]
}
## Parameter order: alpha, sig2, beta_left, beta_right, beta_center, beta_slope
pars.linear <- pull.pars(length(chain.linear$gen), chain.linear, model.linearTheta)
pp.linear <- unname(c(pars.linear$alpha, pars.linear$sig2, pars.linear$beta_intercept, pars.linear$beta_slope))
pp.tanhLin <- unname(c(pars.linear$alpha, pars.linear$sig2, pars.linear$beta_intercept, rnorm(1), runif(1, -10, 10), pars.linear$beta_slope))
opt.sigmoid <- optimx(pp.sigmoid, lik.sigmoid, control=list(maximize=TRUE), method=c('nlminb'), lower=c(0,0,-12,-12,-200, -0.1),
upper=c(2000, 2000, 0, 0, 200, 10))
opt.step <- optimx(pp.step, lik.step, control=list(maximize=TRUE), method=c('nlminb'), lower=c(0,0,-12,-12,-200),
upper=c(2000, 2000, 0, 0, 200))
opt.sigmoid0 <- optimx(pp.sigmoid0, lik.sigmoid0, control=list(maximize=TRUE), method=c('nlminb'), lower=c(0,0,-12,-12,-0.1),
upper=c(2000, 2000, 0, 0, 10))
opt.step0 <- optimx(pp.step0, lik.step0, control=list(maximize=TRUE), method=c('nlminb'), lower=c(0,0,-12,-12),
upper=c(2000, 2000, 0, 0))
opt.linear <- optimx(pp.linear, lik.linear, control=list(maximize=TRUE), method=c('nlminb'), lower=c(0,0,-12,-1),
upper=c(2000, 2000, 0, 1))
opt.tanhLin <- optimx(pp.tanhLin, lik.tanhLin, control=list(maximize=TRUE), method=c('nlminb'), lower=c(0,0,-12, -5,-200, -1), upper=c(2000, 2000, 0, 5, 200, 1))
aics <- list()
aics[['aic.sigmoid']] <- 2*6 - 2*opt.sigmoid$value
aics[['aic.step']] <- 2*5 - 2*opt.step$value
aics[['aic.linear']] <- 2*4 - 2*opt.linear$value
aics[['aic.sigmoid0']] <- 2*5 - 2*opt.sigmoid0$value
aics[['aic.step0']] <- 2*4 - 2*opt.step0$value
aics[['aic.tanhLin']] <- 2*6 - 2*opt.tanhLin$value
aics
return(list(opt.sigmoid=opt.sigmoid, opt.linear=opt.linear, opt.step=opt.step, opt.sigmoid0=opt.sigmoid0, opt.step0=opt.step0, opt.tanhLin=opt.tanhLin, aics=aics))
}require(optimx)
cacheEV <- bayou:::.prepare.ou.univariate(tdEV$phy, tdEV[['lnVs']], SE=0.02, pred=tdEV['binpred'])
cacheD <- bayou:::.prepare.ou.univariate(tdD$phy, tdD[['lnVs']], SE=0.02, pred=tdD['binpred'])
EVopt.allmodels <- lapply(1:100, optimizeModelsOverSimmap, strees.bayou=EVtrees.bayou, chain.sigmoid = chainEV.sigmoid, chain.step=chainEV.step, chain.linear=chainEV.linear, cache = cacheEV)Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Dopt.allmodels <- lapply(1:100, optimizeModelsOverSimmap, strees.bayou=Dtrees.bayou, chain.sigmoid = chainD.sigmoid, chain.step=chainD.step, chain.linear=chainD.linear, cache = cacheD)Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Maximizing -- use negfn and neggrParameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.summarizeAllOpt <- function(opt.allmodels, cutoff=-Inf){
all.sigmoid <- do.call(rbind, lapply(opt.allmodels, function(x) x$opt.sigmoid))
all.step <- do.call(rbind, lapply(opt.allmodels, function(x) x$opt.step))
all.sigmoid0 <- do.call(rbind, lapply(opt.allmodels, function(x) x$opt.sigmoid0))
all.step0 <- do.call(rbind, lapply(opt.allmodels, function(x) x$opt.step0))
all.linear <- do.call(rbind, lapply(opt.allmodels, function(x) x$opt.linear))
all.tanhLin <- do.call(rbind, lapply(opt.allmodels, function(x) x$opt.tanhLin))
all.aics <- do.call(rbind, lapply(opt.allmodels, function(x) do.call(cbind, x$aics)))
all.aics[all.aics < cutoff] <- NA
boxplot(all.aics)
all.aics <- as.data.frame(all.aics)
t.test(all.aics$aic.sigmoid, all.aics$aic.linear, paired = TRUE)
hist(all.aics$aic.linear - all.aics$aic.sigmoid)
tmp <- reshape2::melt(all.aics)
aicws <- aicw(tmp$value)$w
aicws <- round(tapply(aicws, tmp$variable, sum),2)
return(list(sigmoid=all.sigmoid, step=all.step, sigmoid0=all.sigmoid0, step0 = all.step0, linear=all.linear, tanhLin=all.tanhLin, aics=all.aics, aicws=aicws))
}
sumEV <- summarizeAllOpt(EVopt.allmodels)
No id variables; using all as measure variables
sumD <- summarizeAllOpt(Dopt.allmodels)
No id variables; using all as measure variables
sumEV$aicws aic.sigmoid aic.step aic.linear aic.sigmoid0 aic.step0 aic.tanhLin
0.58 0.00 0.06 0.33 0.00 0.03 sumD$aicws aic.sigmoid aic.step aic.linear aic.sigmoid0 aic.step0 aic.tanhLin
0.27 0.01 0.38 0.26 0.02 0.07 dAICs <- apply(sumEV$aics, 1, function(x) x-min(x, na.rm=TRUE))
par(mar=c(10, 4, 1,1))
boxplot(t(dAICs), las=2, xlab="Model", ylab="dAIC", main="EV")
boxplot(t(dAICs), las=2, xlab="Model", ylab="dAIC", main="EV", ylim=c(0,20))
cat("Mean EV dAIC values\n")Mean EV dAIC valuesapply(t(dAICs), 2, mean) aic.sigmoid aic.step aic.linear aic.sigmoid0 aic.step0 aic.tanhLin
0.6305072 43.1810208 3.6929463 1.1235774 41.1810208 4.6331318 dAICs <- apply(sumD$aics, 1, function(x) x-min(x, na.rm=TRUE))
par(mar=c(10, 4, 1,1))
boxplot(t(dAICs), las=2, xlab="Model", ylab="dAIC", main="D")
boxplot(t(dAICs), las=2, xlab="Model", ylab="dAIC",main="D", ylim=c(0,8))
cat("Mean Deciduous dAIC values\n")Mean Deciduous dAIC valuesapply(t(dAICs), 2, mean) aic.sigmoid aic.step aic.linear aic.sigmoid0 aic.step0 aic.tanhLin
3.8844676 9.3537055 0.5131179 1.7110603 7.3537055 3.3364835 plot(td[['tmin.01']], td[['lnVs']], bg=c("tan","green")[as.numeric(td[['phenology']])], main="Temperature vs. vessel size", pch=21)
x <- -600:200
abline(v=0)dum <- lapply(1:nrow(sumEV$sigmoid), function(i) lines(x, sumEV$sigmoid[i, 3] + sumEV$sigmoid[i, 4]/(1 + exp(sumEV$sigmoid[i, 6]*(x-sumEV$sigmoid[i, 5]))), col="green", lty=1))
dum <- lapply(1:nrow(sumEV$sigmoid0), function(i) lines(x, sumEV$sigmoid0[i, 3] + sumEV$sigmoid0[i, 4]/(1 + exp(sumEV$sigmoid0[i, 5]*(x-0))), col="limegreen", lty=1))#dum <- lapply(1:nrow(sumEV$tanhLin), function(i) lines(x, sumEV$tanhLin[i, 3] + sumEV$tanhLin[i,6]*x + sumEV$tanhLin[i,4]*tanh(x-sumEV$tanhLin[i,5]), col="green", lty=1))
dum <- lapply(1:nrow(sumD$linear), function(i) lines(x, sumD$linear[i,3] + sumD$linear[i,4]*x, col="orange2", lty=1))
dum <- lapply(1:nrow(sumD$sigmoid0), function(i) lines(x, sumD$sigmoid0[i, 3] + sumD$sigmoid0[i, 4]/(1 + exp(sumD$sigmoid0[i, 5]*(x-0))), col="orange", lty=1))
#dum <- lapply(1:nrow(sumD$sigmoid0), function(i) lines(x, sumD$sigmoid0[i, 3] + sumD$sigmoid0[i,4]/(1+exp(sumD$sigmoid0[i,5]*x)), col="tan", lty=1))plot(td[['tmin.01']], td[['lnVs']], bg=c("tan","green")[as.numeric(td[['phenology']])], main="Temperature vs. vessel size", pch=21, xlab = "Temp (Cx10)", ylab="Log Vessel Diameter")
x <- -400:200
abline(v=0)dum <- lapply(seq(1, length(chainEV.sigmoid$gen), 1), function(i) lines(x, chainEV.sigmoid$beta_left[i]+chainEV.sigmoid$beta_right[i]/(1 + exp(chainEV.sigmoid$beta_slope[i]*(x-chainEV.sigmoid$beta_center[i]))), col=makeTransparent(rgb(0, 1, 0), alpha=10)))
dum <- lapply(seq(1, length(chainD.linear$gen), 1), function(i) lines(x, chainD.linear$beta_intercept[i]+chainD.linear$beta_slope[i]*x, col=makeTransparent(rgb(0.9, 0.5, 0.2), alpha=10)))
---
title: "Vessel size and continuous predictors"
output: html_notebook
---

An experimental method to deal with continuous predictors and possible alternative functional relationships between them and the trait of interest. In this case, we are modeling vessel size in response to environmental temperature. 

```{r}
## Load in libraries
library(bayou)
library(treeplyr)
library(diversitree)
library(optimx)

## Set working directory
setwd("~/repos/growthforms/analysis")

```


```{r}
td <- readRDS("../output/cleandata/tdOUwie.rds")
td
```


Bayesian update functions:

```{r}
## Test 3 different functions. 
## 1. Step function with 3 parameters:
.updateStepFxTheta <- function(pars, cache, d, ct, move, prior=NULL){
  pars.new <- bayou:::.slidingWindowProposal(cache, pars, d, move, ct)$pars
  hr <- 0
  pars.new$theta <- ifelse(midbins>pars.new$beta_center, pars.new$beta_right, pars.new$beta_left)
  return(list(pars=pars.new, hr=hr))
}

## 2. Sigmoid function with 5 parameters
.updateSigmoidFxTheta <- function(pars, cache, d, ct, move, prior=NULL){
  pars.new <- bayou:::.slidingWindowProposal(cache, pars, d, move, ct)$pars
  hr <- 0
  pars.new$theta <- pars.new$beta_left + pars.new$beta_right/(1 + exp(pars.new$beta_slope * (midbins - pars.new$beta_center)))
  return(list(pars=pars.new, hr=hr))
}

## 3. Linear function with 2 parameters
.updateLinearFxTheta <- function(pars, cache, d, ct, move, prior=NULL){
  pars.new <- bayou:::.slidingWindowProposal(cache, pars, d, move, ct)$pars
  hr <- 0
  pars.new$theta <- pars.new$beta_intercept + pars.new$beta_slope * midbins
  return(list(pars=pars.new, hr=hr))
}
```

Bayesian update functions for dealing with simmap reconstructions
```{r}
## Other needed functions
.newMap <- function(pars, cache, d, ct, prior=NULL, move=NULL){
  pars.new <- pars
  stree <- reorderSimmap(make.simmap(cache$phy, setNames(cache$pred[[1]], cache$phy$tip.label), Q=Q, message=FALSE), "postorder")
  shifts <- sapply(stree$maps, function(x) names(x)[-1])
  pars.new$sb <- unlist(sapply(1:length(shifts), function(x) rep(x, length(shifts[[x]]))))
  pars.new$t2 <- as.numeric(unlist(sapply(stree$maps, function(x) names(x)[-1])))
  pars.new$loc <- unlist(sapply(stree$maps, function(x) cumsum(x)[-1]))
  pars.new$k <- length(pars.new$sb)
  hr <- 0
  return(list(pars=pars.new, hr=Inf))
}

.newMapPresample <- function(pars, cache, d, ct, prior=NULL, move=NULL){
  pars.new <- pars
  index <- sample(1:length(strees.bayou), 1, replace=FALSE)
  pars.new$sb <- strees.bayou[[index]]$sb #unlist(sapply(1:length(shifts), function(x) rep(x, length(shifts[[x]]))))
  pars.new$t2 <- strees.bayou[[index]]$t2 #as.numeric(unlist(sapply(stree$maps, function(x) names(x)[-1])))
  pars.new$loc <- unname(strees.bayou[[index]]$loc) #unlist(sapply(stree$maps, function(x) cumsum(x)[-1]))
  pars.new$k <- length(pars.new$sb)
  hr <- 0
  return(list(pars=pars.new, hr=Inf))
}

## Empty functions to replace the deterministically specified theta values
dnull <- function(x, log=TRUE){
  if(log) 0 else 1
}

rnull <- function(x){
  return(x)
}
```


Place the predictor into bins and transform the tree to unit height.
```{r}
## Set up the bins:
ncat <- 10
pred <- "tmin.01"
trait <- "lnVs"
bins <- seq(min(td[[pred]])-0.1, max(td[[pred]])+0.1, length.out=ncat)
midbins <- (bins[2:length(bins)] + bins[1:(length(bins)-1)])/2
td <- mutate(td, binpred = as.numeric(cut(td[[pred]], breaks=bins, include.lowest=TRUE)))
hist(td[['binpred']])


td$phy$edge.length <- td$phy$edge.length/max(branching.times(td$phy))
tree <- td$phy
dat <- td$dat

```


Fit a meristic model to the binned predictor data and construct a set of stochastic maps. 

```{r}
## Use diversitree to fit a meristic model:
mkn.lik <- make.mkn.meristic(td$phy, td[['binpred']], k = ncat)
mkn.lik <- constrain(mkn.lik, q.up~q.down)
p <- c(0.1)
fit.mkn <- find.mle(mkn.lik, p)
Q <- matrix(0, ncol=ncat-1, nrow=ncat-1)
for(i in 1:(ncat-2)){
  Q[i, i+1] <- fit.mkn$par[1]
  Q[i+1, i] <- fit.mkn$par[1]
}
diag(Q) <- apply(Q, 1, sum)*-1
rownames(Q) <- colnames(Q) <- 1:ncol(Q)
simmap2bayou <- function(stree){
  shifts <- sapply(stree$maps, function(x) names(x)[-1])
  sb <- unlist(sapply(1:length(shifts), function(x) rep(x, length(shifts[[x]]))))
  t2 <- as.numeric(unlist(sapply(stree$maps, function(x) names(x)[-1])))
  loc <- unlist(sapply(stree$maps, function(x) cumsum(x)[-1]))
  return(list(shifts=shifts, sb=sb, t2=t2, loc=loc))
}
#fdfit <- fitDiscrete(td$phy, td[['binpred']], model="meristic")
#Qg <- geiger:::.Qmatrix.from.gfit(fdfit)
td <- reorder(td, "postorder")
stree <- reorderSimmap(make.simmap(td$phy, td[['binpred']], Q=Q), "postorder")
strees <- make.simmap(td$phy, td[['binpred']], Q=Q, nsim=100)
for(i in 1:length(strees)) strees[[i]] <- reorderSimmap(strees[[i]], "postorder")
strees.bayou <- lapply(strees, simmap2bayou)
plotSimmap(stree, col=setNames(cm.colors(ncat), 1:ncat))


model.stepTheta$lik.fn(pp, cache, cache$dat)$loglik

shifts <- sapply(stree$maps, function(x) names(x)[-1])
sb <- unlist(sapply(1:length(shifts), function(x) rep(x, length(shifts[[x]]))))
t2 <- as.numeric(unlist(sapply(stree$maps, function(x) names(x)[-1])))
loc <- unname(unlist(sapply(stree$maps, function(x) cumsum(x)[-1])))
tmp <- lm(td[[trait]]~td[[pred]]+I(td[[pred]]^2)+I(td[[pred]]^3))

saveRDS(strees.bayou, "../output/OUwie/strees.bayou.rds")

```

```{r}
strees.bayou <- readRDS("../output/OUwie/strees.bayou.rds")
```


```{r}
## Starting parameters for each function type:
## Step function
step.starts <- c(0, -8, -4)
startpar.step <- list(alpha=1, sig2=0.5, 
                 beta_left=step.starts[2], 
                 beta_right=step.starts[3],
                 beta_center=step.starts[1],
                 k=length(sb), ntheta=ncat-1, 
                 theta=ifelse(midbins > step.starts[1], step.starts[3], step.starts[2]),
                 sb=sb, loc=loc, t2=t2)
plot(midbins, startpar.step$theta)


sigmoid.starts <- list(beta_left = -2, beta_right = -6, beta_center = 0, beta_slope = 0.02)
startpar.sigmoid <- list(alpha=1, sig2=0.5, 
                 beta_left=sigmoid.starts$beta_left, 
                 beta_right=sigmoid.starts$beta_right,
                 beta_center=sigmoid.starts$beta_center,
                 beta_slope = sigmoid.starts$beta_slope,
                 k=length(sb), ntheta=ncat-1, 
                 theta= sigmoid.starts$beta_left + sigmoid.starts$beta_right/(1 + exp(sigmoid.starts$beta_slope * (midbins - sigmoid.starts$beta_center))),
                 sb=sb, loc=loc, t2=t2)
plot(midbins, startpar.sigmoid$theta)

linear.starts <- list(beta_intercept = -5, beta_slope = 0.01)
startpar.linear <- list(alpha=1, sig2=0.5, 
                 beta_intercept=linear.starts$beta_intercept, 
                 beta_slope=linear.starts$beta_slope,
                 k=length(sb), ntheta=ncat-1, 
                 theta= linear.starts$beta_intercept + linear.starts$beta_slope*midbins,
                 sb=sb, loc=loc, t2=t2)
plot(midbins, startpar.linear$theta)

```


```{r}
## Set priors
prior.step <- make.prior(td$phy, plot.prior = FALSE, 
                          dists=list(dalpha="dlnorm", dsig2="dhalfcauchy", 
                                     dbeta_left="dunif",
                                     dbeta_right="dunif",
                                     dbeta_center="dunif",
                                     dsb="fixed", dk="fixed", 
                                     dtheta="dnull", dloc="fixed"), 
                          param=list(dalpha=list(meanlog=1, sdlog=1), dsig2=list(scale=0.1), 
                                     dbeta_left=list(min=-12, max=0),
                                     dbeta_right=list(min=-12, max=0),
                                     dbeta_center=list(min=-100, max=100),
                                     dk="fixed",
                                     dsb="fixed", 
                                     dtheta=list(), dloc="fixed"),
                          fixed=list(k=length(sb), ntheta=ncat-1, sb=sb, loc=loc, t2=t2)
)

prior.sigmoid <- make.prior(td$phy, plot.prior = FALSE, 
                          dists=list(dalpha="dlnorm", dsig2="dhalfcauchy", 
                                     dbeta_left="dunif",
                                     dbeta_right="dunif",
                                     dbeta_center="dunif",
                                     dbeta_slope="dlnorm",
                                     dsb="fixed", dk="fixed", 
                                     dtheta="dnull", dloc="fixed"), 
                          param=list(dalpha=list(meanlog=1, sdlog=1), dsig2=list(scale=0.1), 
                                     dbeta_left=list(min=-12, max=0),
                                     dbeta_right=list(min=-12, max=0),
                                     dbeta_center=list(min=-100, max=100),
                                     dbeta_slope=list(meanlog=-3, sdlog=1),
                                     dk="fixed",
                                     dsb="fixed", 
                                     dtheta=list(), dloc="fixed"),
                          fixed=list(k=length(sb), ntheta=ncat-1, sb=sb, loc=loc, t2=t2)
)

prior.linear <- make.prior(td$phy, plot.prior = FALSE, 
                          dists=list(dalpha="dlnorm", dsig2="dhalfcauchy", 
                                     dbeta_intercept="dnorm",
                                     dbeta_slope="dnorm",
                                     dsb="fixed", dk="fixed", 
                                     dtheta="dnull", dloc="fixed"), 
                          param=list(dalpha=list(meanlog=1, sdlog=1), dsig2=list(scale=0.1), 
                                     dbeta_intercept=list(mean=-6, sd=1.5),
                                     dbeta_slope=list(mean=0, sd=0.1),
                                     dk="fixed",
                                     dsb="fixed", 
                                     dtheta=list(), dloc="fixed"),
                          fixed=list(k=length(sb), ntheta=ncat-1, sb=sb, loc=loc, t2=t2)
)
```



```{r}
model.stepTheta <- list(moves = list(alpha=".multiplierProposal", 
                                      sig2=".multiplierProposal",
                                beta_left=".updateStepFxTheta",
                                beta_right=".updateStepFxTheta",
                                beta_center=".updateStepFxTheta",
                                theta=".newMapPresample"),
                   
                   control.weights = list(alpha=10, sig2=10,
                                          beta_left=10,
                                          beta_right=10,
                                          beta_center=10,
                                          theta=1,
                                          k=0),
                   D = list(alpha=1, sig2=1, 
                            beta_left=0.2,
                            beta_right=0.2,
                            beta_center=20,
                            theta = 1),
                   parorder = c("alpha", "sig2", "beta_left", "beta_right", "beta_center", "theta", "k", "ntheta"),
                   rjpars = c(),
                   shiftpars = c("sb", "loc", "t2"),
                   lik.fn = bayou.lik)

model.stepTheta$monitor.fn <- function(i, lik, pr, pars, accept, accept.type, j){
  names <- c("gen", "lnL", "prior", "alpha","sig2", "beta_left", "beta_right", "beta_center", "k") #The parameters we want to print
  string <- "%-8i%-8.2f%-8.2f%-8.2f%-8.2f%-8.2f%-8.2f%-8.2f%-8i" #The types of each parameter
  acceptratios <- tapply(accept, accept.type, mean)
  names <- c(names, names(acceptratios))
  if(j==0){
    cat(sprintf("%-7.7s", names), "\n", sep=" ")                           
  }
  cat(sprintf(string, i, lik, pr, pars$alpha, pars$sig2[1], pars$beta_left, pars$beta_right, pars$beta_center, pars$k), sprintf("%-8.2f", acceptratios),"\n", sep="")
}

```


```{r}
model.sigmoidTheta <- list(moves = list(alpha=".multiplierProposal", 
                                      sig2=".multiplierProposal",
                                  beta_left=".updateSigmoidFxTheta",
                                beta_right=".updateSigmoidFxTheta",
                                beta_center=".updateSigmoidFxTheta",
                                beta_slope=".updateSigmoidFxTheta",
                                theta=".newMapPresample"),
                   
                   control.weights = list(alpha=10, sig2=10,
                                          beta_left=10,
                                          beta_right=10,
                                          beta_center=10,
                                          beta_slope=10,
                                          theta=1,
                                          k=0),
                   D = list(alpha=1, sig2=1, 
                            beta_left=0.2,
                            beta_right=0.2,
                            beta_center=20,
                            beta_slope=0.005,
                            theta = 1),
                   parorder = c("alpha", "sig2", "beta_left", "beta_right", "beta_center", "beta_slope", "theta", "k", "ntheta"),
                   rjpars = c(),
                   shiftpars = c("sb", "loc", "t2"),
                   lik.fn = bayou.lik)

model.sigmoidTheta$monitor.fn <- function(i, lik, pr, pars, accept, accept.type, j){
  names <- c("gen", "lnL", "prior", "alpha","sig2", "beta_left", "beta_right", "beta_center", "beta_slope", "k") #The parameters we want to print
  string <- "%-8i%-8.2f%-8.2f%-8.2f%-8.2f%-8.2f%-8.2f%-8.2f%-8.2f%-8i" #The types of each parameter
  acceptratios <- tapply(accept, accept.type, mean)
  names <- c(names, names(acceptratios))
  if(j==0){
    cat(sprintf("%-7.7s", names), "\n", sep=" ")                           
  }
  cat(sprintf(string, i, lik, pr, pars$alpha, pars$sig2[1], pars$beta_left, pars$beta_right, pars$beta_center, pars$beta_slope, pars$k), sprintf("%-8.2f", acceptratios),"\n", sep="")
}

```

```{r}
model.linearTheta <- list(moves = list(alpha=".multiplierProposal", 
                                      sig2=".multiplierProposal",
                                  beta_intercept=".updateLinearFxTheta",
                                beta_slope=".updateLinearFxTheta",
                                theta=".newMapPresample"),
                   
                   control.weights = list(alpha=10, sig2=10,
                                          beta_intercept=10,
                                          beta_slope=10,
                                          theta=1,
                                          k=0),
                   D = list(alpha=1, sig2=1, 
                            beta_intercept=0.5,
                            beta_slope=0.005,
                            theta = 1),
                   parorder = c("alpha", "sig2", "beta_intercept", "beta_slope", "theta", "k", "ntheta"),
                   rjpars = c(),
                   shiftpars = c("sb", "loc", "t2"),
                   lik.fn = bayou.lik)

model.linearTheta$monitor.fn <- function(i, lik, pr, pars, accept, accept.type, j){
  names <- c("gen", "lnL", "prior", "alpha","sig2", "beta_intercept", "beta_slope", "k") #The parameters we want to print
  string <- "%-8i%-8.2f%-8.2f%-8.2f%-8.2f%-8.2f%-8.2f%-8i" #The types of each parameter
  acceptratios <- tapply(accept, accept.type, mean)
  names <- c(names, names(acceptratios))
  if(j==0){
    cat(sprintf("%-7.7s", names), "\n", sep=" ")                           
  }
  cat(sprintf(string, i, lik, pr, pars$alpha, pars$sig2[1], pars$beta_intercept, pars$beta_slope, pars$k), sprintf("%-8.2f", acceptratios),"\n", sep="")
}

```


```{r}
## Just a bunch of tests

cache <- bayou:::.prepare.ou.univariate(td$phy, td[['lnVs']], SE=0, pred=td['binpred'])
prior.step(startpar.step)
model.stepTheta$lik.fn(startpar.step, cache, cache$dat)$loglik
ct <- bayou:::.buildControl(startpar.step, prior.step, move.weights=model.stepTheta$control.weights)
pars.new <- .newMapPresample(startpar.step, cache, d=model.stepTheta$D$theta, ct = ct)$pars
prior.step(pars.new)
model.stepTheta$lik.fn(pars.new, cache, cache$dat)$loglik
pp <- pars.new
prior.step(pp)
model.stepTheta$lik.fn(pp, cache, cache$dat)$loglik

new.pp <- .newMapPresample(pp, cache, 1, ct)$pars
model.stepTheta$lik.fn(new.pp, cache, cache$dat)$loglik

prior.linear(startpar.linear)
new.pp <- .newMapPresample(startpar.linear, cache, 1, ct)$pars
model.linearTheta$lik.fn(new.pp, cache, cache$dat)$loglik




```


```{r}

mymcmc.step <- bayou.makeMCMC(td$phy, td[[trait]], pred=td['binpred'], SE=0.02, model=model.stepTheta, prior=prior.step, startpar=startpar.step, new.dir=TRUE, outname="stepTheta_r2", plot.freq=NULL, ticker.freq=2000, samp = 100)


mymcmc.sigmoid <- bayou.makeMCMC(td$phy, td[[trait]], pred=td['binpred'], SE=0.02, model=model.sigmoidTheta, prior=prior.sigmoid, startpar=startpar.sigmoid, new.dir=TRUE, outname="sigmoidTheta_r2", plot.freq=NULL, ticker.freq=2000, samp = 100)


mymcmc.linear <- bayou.makeMCMC(td$phy, td[[trait]], pred=td['binpred'], SE=0.02, model=model.linearTheta, prior=prior.linear, startpar=startpar.linear, new.dir=TRUE, outname="linearTheta_r2", plot.freq=NULL, ticker.freq=2000, samp = 100)

```


```{r}
mymcmc.step$run(100000)
chain.step <- mymcmc.step$load()
chain.step <- set.burnin(chain.step, 0.3)
plot(chain.step)
```


```{r}
plot(midbins, startpar.step$theta, ylim=c(-10, 0), type="n")
x <- -350:200
points(td[['tmin.025']], td[['lnVs']], pch=21, bg="gray50")
lapply(seq(1, length(chain.step$gen), 10), function(i) lines(x, ifelse(x>chain.step$beta_center[i], chain.step$beta_right[i], chain.step$beta_left[i]), col=makeTransparent(rgb(1, 0, 0), alpha=10)))
```

```{r}
mymcmc.sigmoid$run(100000)
chain.sigmoid <- mymcmc.sigmoid$load()
chain.sigmoid <- set.burnin(chain.sigmoid, 0.3)
plot(chain.sigmoid)
```


```{r}
plot(midbins, startpar.step$theta, ylim=c(-10, 0), type="n")
x <- -350:200
points(td[['tmin.025']], td[['lnVs']], pch=21, bg="gray50")
lapply(seq(1, length(chain.sigmoid$gen), 1), function(i) lines(x, chain.sigmoid$beta_left[i]+chain.sigmoid$beta_right[i]/(1 + exp(chain.sigmoid$beta_slope[i]*(x-chain.sigmoid$beta_center[i]))), col=makeTransparent(rgb(1, 0, 0), alpha=10)))
```



```{r}
mymcmc.linear$run(100000)
chain.linear <- mymcmc.linear$load()
chain.linear <- set.burnin(chain.linear, 0.3)
plot(chain.linear)
```


```{r}
plot(midbins, startpar.linear$theta, ylim=c(-10, 0), type="n")
x <- -350:200
points(td[['tmin.025']], td[['lnVs']], pch=21, bg="gray50")
lapply(seq(1, length(chain.linear$gen), 1), function(i) lines(x, chain.linear$beta_intercept[i]+chain.linear$beta_slope[i]*x, col=makeTransparent(rgb(1, 0, 0), alpha=10)))
```




```{r}
plot(chain.step$lnL, type="n", ylim=c(-700, -500))
lines(1:length(chain.sigmoid$lnL), chain.sigmoid$lnL, col="green")
lines(1:length(chain.linear$lnL), chain.linear$lnL, col="blue")
lines(1:length(chain.step$lnL), chain.step$lnL, col="red")
saveRDS(chain.sigmoid, "../output/OUwie/chain.sigmoid.rds")
saveRDS(chain.linear, "../output/OUwie/chain.linear.rds")
saveRDS(chain.step, "../output/OUwie/chain.step.rds")
chain.sigmoid <- readRDS("../output/OUwie/chain.sigmoid.rds")
chain.linear <- readRDS("../output/OUwie/chain.linear.rds")
chain.step <- readRDS("../output/OUwie/chain.step.rds")

```

Let's do a likelihood based analysis. 

```{r}
library(optimx)
i <- 10
sb <- strees.bayou[[i]]$sb
t2 <- strees.bayou[[i]]$t2
loc <- strees.bayou[[i]]$loc
k <- length(sb)
ntheta <- ncat - 1
lik.sigmoid <- function(pp){
  pars <- list(alpha = pp[1],
               sig2 = pp[2], 
               beta_left = pp[3],
               beta_right = pp[4], 
               beta_center = pp[5],
               beta_slope = pp[6],
               k = k, 
               ntheta=ntheta,
               theta = pp[3] + pp[4]/(1 + exp(pp[6] * (midbins - pp[5]))),
               sb = sb, 
               loc=loc,
               t2=t2)
  model.sigmoidTheta$lik.fn(pars, cache, cache$dat)$loglik[,1]
}
## Parameter order: alpha, sig2, beta_left, beta_right, beta_center, beta_slope
pars.sigmoid <- pull.pars(length(chain.sigmoid$gen), chain.sigmoid, model.sigmoidTheta)
pp.sigmoid <- unname(c(pars.sigmoid$alpha, pars.sigmoid$sig2, pars.sigmoid$beta_left, pars.sigmoid$beta_right, pars.sigmoid$beta_center, pars.sigmoid$beta_slope))

lik.step <- function(pp){
  pars <- list(alpha = pp[1],
               sig2 = pp[2], 
               beta_left = pp[3],
               beta_right = pp[4], 
               beta_center = pp[5],
               k = k, 
               ntheta=ntheta,
               theta = ifelse(midbins>pp[5], pp[4], pp[3]),
               sb = sb, 
               loc=loc,
               t2=t2)
  model.stepTheta$lik.fn(pars, cache, cache$dat)$loglik[,1]
}
## Parameter order: alpha, sig2, beta_left, beta_right, beta_center, beta_slope
pars.step <- pull.pars(length(chain.step$gen), chain.step, model.stepTheta)
pp.step <- unname(c(pars.step$alpha, pars.step$sig2, pars.step$beta_left, pars.step$beta_right, pars.step$beta_center))

lik.linear <- function(pp){
  pars <- list(alpha = pp[1],
               sig2 = pp[2], 
               beta_intercept = pp[3],
               beta_slope = pp[4], 
               k = k, 
               ntheta=ntheta,
               theta = pp[3] + pp[4]*midbins,
               sb = sb, 
               loc=loc,
               t2=t2)
  model.linearTheta$lik.fn(pars, cache, cache$dat)$loglik[,1]
}
## Parameter order: alpha, sig2, beta_left, beta_right, beta_center, beta_slope
pars.linear <- pull.pars(length(chain.linear$gen), chain.linear, model.linearTheta)
pp.linear <- unname(c(pars.linear$alpha, pars.linear$sig2, pars.linear$beta_intercept, pars.linear$beta_slope))

```


```{r}
opt.sigmoid <- optimx(pp.sigmoid, lik.sigmoid, control=list(maximize=TRUE), method=c('nlminb'), lower=c(0,0,-12,-12,-200, -0.1), upper=c(1000, 1000, 0, 0, 200, 10))
opt.step <- optimx(pp.step, lik.step, control=list(maximize=TRUE), method=c('nlminb'), lower=c(0,0,-12,-12,-200), 
                      upper=c(1000, 1000, 0, 0, 200))
opt.linear <- optimx(pp.linear, lik.linear, control=list(maximize=TRUE), method=c('nlminb'), lower=c(0,0,-12,-1), 
                      upper=c(1000, 1000, 0, 1))

```

```{r}
aics <- list()
aics[['aic.sigmoid']] <- 2*6 - 2*opt.sigmoid$value
aics[['aic.step']] <- 2*5 - 2*opt.step$value
aics[['aic.linear']] <- 2*4 - 2*opt.linear$value
aics
```

```{r}
plot(midbins, startpar.linear$theta, ylim=c(-10, 0), type="n")
x <- -350:200
points(td[['tmin.025']], td[['lnVs']], pch=21, bg="gray50")
Vy.linear <- sqrt(opt.linear$p2/(2*opt.linear$p1))
lines(x, opt.linear$p3+opt.linear$p4*x, col=makeTransparent(rgb(0, 0, 1), alpha=255), lwd=2)
lines(x, (opt.linear$p3+opt.linear$p4*x) + 2*Vy.linear, col=makeTransparent(rgb(0, 0, 1), alpha=255), lwd=2, lty=2)
lines(x, (opt.linear$p3+opt.linear$p4*x) - 2*Vy.linear, col=makeTransparent(rgb(0, 0, 1), alpha=255), lwd=2, lty=2)

Vy.sigmoid <- opt.sigmoid$p2/(2*opt.sigmoid$p1)
lines(x, opt.sigmoid$p3+opt.sigmoid$p4/(1 + exp(opt.sigmoid$p6*(x-opt.sigmoid$p5))), col=makeTransparent(rgb(0, 1, 0), alpha=255), lwd=2)
lines(x, (opt.sigmoid$p3+opt.sigmoid$p4/(1 + exp(opt.sigmoid$p6*(x-opt.sigmoid$p5)))) + 2*Vy.sigmoid, col=makeTransparent(rgb(0, 1, 0), alpha=255), lwd=2, lty=2)
lines(x, (opt.sigmoid$p3+opt.sigmoid$p4/(1 + exp(opt.sigmoid$p6*(x-opt.sigmoid$p5)))) - 2*Vy.sigmoid, col=makeTransparent(rgb(0, 1, 0), alpha=255), lwd=2, lty=2)

Vy.step <- opt.step$p2/(2*opt.step$p1)
lines(x, ifelse(x > opt.step$p5, opt.step$p4, opt.step$p3), col=makeTransparent(rgb(1, 0, 0), alpha=255), lwd=2)
lines(x, ifelse(x > opt.step$p5, opt.step$p4, opt.step$p3) + 2*Vy.step, col=makeTransparent(rgb(1, 0, 0), alpha=255), lwd=2, lty=2)
lines(x, ifelse(x > opt.step$p5, opt.step$p4, opt.step$p3) - 2*Vy.step, col=makeTransparent(rgb(1, 0, 0), alpha=255), lwd=2, lty=2)

```


```{r}
## Script it over all trees. 
optimizeModelsOverSimmap <- function(i){
  sb <- strees.bayou[[i]]$sb
  t2 <- strees.bayou[[i]]$t2
  loc <- strees.bayou[[i]]$loc
  k <- length(sb)
  ntheta <- ncat - 1
lik.sigmoid <- function(pp){
  pars <- list(alpha = pp[1],
               sig2 = pp[2], 
               beta_left = pp[3],
               beta_right = pp[4], 
               beta_center = pp[5],
               beta_slope = pp[6],
               k = k, 
               ntheta=ntheta,
               theta = pp[3] + pp[4]/(1 + exp(pp[6] * (midbins - pp[5]))),
               sb = sb, 
               loc=loc,
               t2=t2)
  model.sigmoidTheta$lik.fn(pars, cache, cache$dat)$loglik[,1]
}

lik.sigmoid0 <- function(pp){
  pars <- list(alpha = pp[1],
               sig2 = pp[2], 
               beta_left = pp[3],
               beta_right = pp[4], 
               beta_center = 0,
               beta_slope = pp[5],
               k = k, 
               ntheta=ntheta,
               theta = pp[3] + pp[4]/(1 + exp(pp[5] * (midbins - 0))),
               sb = sb, 
               loc=loc,
               t2=t2)
  model.sigmoidTheta$lik.fn(pars, cache, cache$dat)$loglik[,1]
}

lik.tanhLin <- function(pp){
  pars <- list(alpha = pp[1],
               sig2 = pp[2], 
               beta_left = pp[3],
               beta_right = pp[4], 
               beta_center = pp[5],
               beta_slope = pp[6],
               k = k, 
               ntheta=ntheta,
               theta = pp[3] + pp[6]*midbins + pp[4]*tanh(midbins - pp[5]),
               sb = sb, 
               loc=loc,
               t2=t2)
  model.sigmoidTheta$lik.fn(pars, cache, cache$dat)$loglik[,1]
}

## Parameter order: alpha, sig2, beta_left, beta_right, beta_center, beta_slope
pars.sigmoid <- pull.pars(length(chain.sigmoid$gen), chain.sigmoid, model.sigmoidTheta)
pp.sigmoid <- unname(c(pars.sigmoid$alpha, pars.sigmoid$sig2, pars.sigmoid$beta_left, pars.sigmoid$beta_right, pars.sigmoid$beta_center, pars.sigmoid$beta_slope))

pp.sigmoid0 <- pp.sigmoid[-5]

lik.step <- function(pp){
  pars <- list(alpha = pp[1],
               sig2 = pp[2], 
               beta_left = pp[3],
               beta_right = pp[4], 
               beta_center = pp[5],
               k = k, 
               ntheta=ntheta,
               theta = ifelse(midbins>pp[5], pp[4], pp[3]),
               sb = sb, 
               loc=loc,
               t2=t2)
  model.stepTheta$lik.fn(pars, cache, cache$dat)$loglik[,1]
}

lik.step0 <- function(pp){
  pars <- list(alpha = pp[1],
               sig2 = pp[2], 
               beta_left = pp[3],
               beta_right = pp[4], 
               beta_center = 0,
               k = k, 
               ntheta=ntheta,
               theta = ifelse(midbins>0, pp[4], pp[3]),
               sb = sb, 
               loc=loc,
               t2=t2)
  model.stepTheta$lik.fn(pars, cache, cache$dat)$loglik[,1]
}
## Parameter order: alpha, sig2, beta_left, beta_right, beta_center, beta_slope
pars.step <- pull.pars(length(chain.step$gen), chain.step, model.stepTheta)
pp.step <- unname(c(pars.step$alpha, pars.step$sig2, pars.step$beta_left, pars.step$beta_right, pars.step$beta_center))
pp.step0 <- pp.step[-5]

lik.linear <- function(pp){
  pars <- list(alpha = pp[1],
               sig2 = pp[2], 
               beta_intercept = pp[3],
               beta_slope = pp[4], 
               k = k, 
               ntheta=ntheta,
               theta = pp[3] + pp[4]*midbins,
               sb = sb, 
               loc=loc,
               t2=t2)
  model.linearTheta$lik.fn(pars, cache, cache$dat)$loglik[,1]
}
## Parameter order: alpha, sig2, beta_left, beta_right, beta_center, beta_slope
pars.linear <- pull.pars(length(chain.linear$gen), chain.linear, model.linearTheta)
pp.linear <- unname(c(pars.linear$alpha, pars.linear$sig2, pars.linear$beta_intercept, pars.linear$beta_slope))

pp.tanhLin <- unname(c(pars.linear$alpha, pars.linear$sig2, pars.linear$beta_intercept, rnorm(1), runif(1, -10, 10), pars.linear$beta_slope))

opt.sigmoid <- optimx(pp.sigmoid, lik.sigmoid, control=list(maximize=TRUE), method=c('nlminb'), lower=c(0,0,-12,-12,-200, -0.1), 
                      upper=c(2000, 2000, 0, 0, 200, 10))
opt.step <- optimx(pp.step, lik.step, control=list(maximize=TRUE), method=c('nlminb'), lower=c(0,0,-12,-12,-200), 
                      upper=c(2000, 2000, 0, 0, 200))
opt.sigmoid0 <- optimx(pp.sigmoid0, lik.sigmoid0, control=list(maximize=TRUE), method=c('nlminb'), lower=c(0,0,-12,-12,-0.1), 
                      upper=c(2000, 2000, 0, 0, 10))
opt.step0 <- optimx(pp.step0, lik.step0, control=list(maximize=TRUE), method=c('nlminb'), lower=c(0,0,-12,-12), 
                      upper=c(2000, 2000, 0, 0))
opt.linear <- optimx(pp.linear, lik.linear, control=list(maximize=TRUE), method=c('nlminb'), lower=c(0,0,-12,-1), 
                      upper=c(2000, 2000, 0, 1))

opt.tanhLin <- optimx(pp.tanhLin, lik.tanhLin, control=list(maximize=TRUE), method=c('nlminb'), lower=c(0,0,-12, -5,-200, -1), upper=c(2000, 2000, 0, 5, 200, 1))

aics <- list()
aics[['aic.sigmoid']] <- 2*6 - 2*opt.sigmoid$value
aics[['aic.step']] <- 2*5 - 2*opt.step$value
aics[['aic.linear']] <- 2*4 - 2*opt.linear$value
aics[['aic.sigmoid0']] <- 2*5 - 2*opt.sigmoid0$value
aics[['aic.step0']] <- 2*4 - 2*opt.step0$value
aics[['aic.tanhLin']] <- 2*6 - 2*opt.tanhLin$value
aics

return(list(opt.sigmoid=opt.sigmoid, opt.linear=opt.linear, opt.step=opt.step, opt.sigmoid0=opt.sigmoid0, opt.step0=opt.step0, opt.tanhLin=opt.tanhLin, aics=aics))
}


```


```{r}

opt.allmodels <- lapply(1:100, optimizeModelsOverSimmap)

```

```{r}
all.sigmoid <- do.call(rbind, lapply(opt.allmodels, function(x) x$opt.sigmoid))
all.step <- do.call(rbind, lapply(opt.allmodels, function(x) x$opt.step))
all.sigmoid0 <- do.call(rbind, lapply(opt.allmodels, function(x) x$opt.sigmoid0))
all.step0 <- do.call(rbind, lapply(opt.allmodels, function(x) x$opt.step0))
all.linear <- do.call(rbind, lapply(opt.allmodels, function(x) x$opt.linear))
all.tanhLin <- do.call(rbind, lapply(opt.allmodels, function(x) x$opt.tanhLin))
all.aics <- do.call(rbind, lapply(opt.allmodels, function(x) do.call(cbind, x$aics)))
boxplot(all.aics)
all.aics <- as.data.frame(all.aics)
t.test(all.aics$aic.sigmoid, all.aics$aic.linear, paired = TRUE)
tmp <- reshape2::melt(all.aics)
cat("Akaike weights\n")
aicws <- aicw(tmp$value)$w
round(tapply(aicws, tmp$variable, sum),2)

dAICs <- apply(all.aics, 1, function(x) x-min(x, na.rm=TRUE))
par(mar=c(10, 4, 1,1))
boxplot(t(dAICs), las=2, xlab="Model", ylab="dAIC")
boxplot(t(dAICs), las=2, xlab="Model", ylab="dAIC", ylim=c(0,8))
cat("Mean dAIC values\n")
apply(t(dAICs), 2, mean)

```

# Independent analyses for Deciduous vs. Evergreen
Let's filter by phenology and analyze each independently. This is not ideal, since transitions occur between deciduous and evergreen species, and are not accounted for when we filter this way. However, if phylogenetic half-lives are small, this is probably of negligible importance. 

```{r}
## Set up the bins:
ncat <- 10
pred <- "tmin.01"
trait <- "lnVs"
bins <- seq(min(td[[pred]])-0.1, max(td[[pred]])+0.1, length.out=ncat)
midbins <- (bins[2:length(bins)] + bins[1:(length(bins)-1)])/2
td <- mutate(td, binpred = as.numeric(cut(td[[pred]], breaks=bins, include.lowest=TRUE)))
hist(td[['binpred']])
bins

td$phy$edge.length <- td$phy$edge.length/max(branching.times(td$phy))
tree <- td$phy
dat <- td$dat

```



```{r}
tdEV <- filter(td, phenology == "EV")
tdD <- filter(td, phenology == "D")
tdEV$phy <- reorder(tdEV$phy, "postorder")
tdD$phy <- reorder(tdD$phy, "postorder")
#tdEV$phy$edge.length <- tdEV$phy$edge.length/max(branching.times(tdEV$phy))*100

## Use diversitree to fit a meristic model:
mknEV.lik <- make.mkn.meristic(tdEV$phy, tdEV[['binpred']], k = ncat)
mknD.lik <- make.mkn.meristic(tdD$phy, tdD[['binpred']], k = ncat)
mknEV.lik <- constrain(mknEV.lik, q.up~q.down)
mknD.lik <- constrain(mknD.lik, q.up~q.down)
p <- c(0.1)
EVfit.mkn <- find.mle(mknEV.lik, p)
Dfit.mkn <- find.mle(mknD.lik, p)

Q.EV <- matrix(0, ncol=ncat-1, nrow=ncat-1)
for(i in 1:(ncat-2)){
  Q.EV[i, i+1] <- EVfit.mkn$par[1]
  Q.EV[i+1, i] <- EVfit.mkn$par[1]
}

Q.D <- matrix(0, ncol=ncat-1, nrow=ncat-1)
for(i in 1:(ncat-2)){
  Q.D[i, i+1] <- Dfit.mkn$par[1]
  Q.D[i+1, i] <- Dfit.mkn$par[1]
}

diag(Q.EV) <- apply(Q.EV, 1, sum)*-1
diag(Q.D) <- apply(Q.D, 1, sum)*-1

rownames(Q.EV) <- colnames(Q.EV) <- 1:ncol(Q.EV)
rownames(Q.D) <- colnames(Q.D) <- 1:ncol(Q.D)

simmap2bayou <- function(stree){
  shifts <- sapply(stree$maps, function(x) names(x)[-1])
  sb <- unlist(sapply(1:length(shifts), function(x) rep(x, length(shifts[[x]]))))
  t2 <- as.numeric(unlist(sapply(stree$maps, function(x) names(x)[-1])))
  loc <- unlist(sapply(stree$maps, function(x) cumsum(x)[-1]))
  return(list(shifts=shifts, sb=sb, t2=t2, loc=loc))
}
#fdfit <- fitDiscrete(td$phy, td[['binpred']], model="meristic")
#Qg <- geiger:::.Qmatrix.from.gfit(fdfit)
#stree <- reorderSimmap(make.simmap(td$phy, td[['binpred']], Q=Q), "postorder")
strees.EV <- make.simmap(tdEV$phy, tdEV[['binpred']], Q=Q.EV, nsim=100)
strees.D <- make.simmap(tdD$phy, tdD[['binpred']], Q=Q.D, nsim=100)

for(i in 1:length(strees.EV)) strees.EV[[i]] <- reorderSimmap(strees.EV[[i]], "postorder")
for(i in 1:length(strees.D)) strees.D[[i]] <- reorderSimmap(strees.D[[i]], "postorder")

EVtrees.bayou <- lapply(strees.EV, simmap2bayou)
Dtrees.bayou <- lapply(strees.D, simmap2bayou)

```

```{r}
saveRDS(EVtrees.bayou, file="../output/OUwie/EVtrees.rds")
saveRDS(Dtrees.bayou, file="../output/OUwie/Dtrees.rds")
EVtrees.bayou <- readRDS(file="../output/OUwie/EVtrees.rds")
Dtrees.bayou <- readRDS(file="../output/OUwie/Dtrees.rds")


```


```{r}
## Starting parameters for each function type:
## Step function
step.starts <- c(0, -8, -4)
startpar.stepEVD <- list(
  EV = list(alpha=1, sig2=1, 
                 beta_left=step.starts[2], 
                 beta_right=step.starts[3],
                 beta_center=step.starts[1],
                 k=length(EVtrees.bayou[[1]]$sb), ntheta=ncat-1, 
                 theta=ifelse(midbins > step.starts[1], step.starts[3], step.starts[2]),
                 sb=EVtrees.bayou[[1]]$sb, loc=EVtrees.bayou[[1]]$loc, t2=EVtrees.bayou[[1]]$t2), 
  D = list(alpha=1, sig2=1, 
                 beta_left=step.starts[2], 
                 beta_right=step.starts[3],
                 beta_center=step.starts[1],
                 k=length(Dtrees.bayou[[1]]$sb), ntheta=ncat-1, 
                 theta=ifelse(midbins > step.starts[1], step.starts[3], step.starts[2]),
                 sb=Dtrees.bayou[[1]]$sb, loc=Dtrees.bayou[[1]]$loc, t2=Dtrees.bayou[[1]]$t2)
)

plot(midbins, startpar.stepEVD$EV$theta)


sigmoid.starts <- list(beta_left = -2, beta_right = -6, beta_center = 0, beta_slope = 0.02)
startpar.sigmoidEVD <- list(
  EV = list(alpha=1, sig2=0.5, 
                 beta_left=sigmoid.starts$beta_left, 
                 beta_right=sigmoid.starts$beta_right,
                 beta_center=sigmoid.starts$beta_center,
                 beta_slope = sigmoid.starts$beta_slope,
                 k=length(EVtrees.bayou[[1]]$sb), ntheta=ncat-1, 
                 theta= sigmoid.starts$beta_left + sigmoid.starts$beta_right/(1 + exp(sigmoid.starts$beta_slope * (midbins - sigmoid.starts$beta_center))),
                 sb=EVtrees.bayou[[1]]$sb, loc=EVtrees.bayou[[1]]$loc, t2=EVtrees.bayou[[1]]$t2),
  D = list(alpha=1, sig2=0.5, 
                 beta_left=sigmoid.starts$beta_left, 
                 beta_right=sigmoid.starts$beta_right,
                 beta_center=sigmoid.starts$beta_center,
                 beta_slope = sigmoid.starts$beta_slope,
                 k=length(Dtrees.bayou[[1]]$sb), ntheta=ncat-1, 
                 theta= sigmoid.starts$beta_left + sigmoid.starts$beta_right/(1 + exp(sigmoid.starts$beta_slope * (midbins - sigmoid.starts$beta_center))),
                 sb=Dtrees.bayou[[1]]$sb, loc=Dtrees.bayou[[1]]$loc, t2=Dtrees.bayou[[1]]$t2)
)
plot(midbins, startpar.sigmoidEVD$EV$theta)

linear.starts <- list(beta_intercept = -5, beta_slope = 0.01)
startpar.linearEVD <- list(
  EV = list(alpha=1, sig2=0.5, 
                 beta_intercept=linear.starts$beta_intercept, 
                 beta_slope=linear.starts$beta_slope,
                 k=length(EVtrees.bayou[[1]]$sb), ntheta=ncat-1, 
                 theta= linear.starts$beta_intercept + linear.starts$beta_slope*midbins,
                 sb=EVtrees.bayou[[1]]$sb, loc=EVtrees.bayou[[1]]$loc, t2=EVtrees.bayou[[1]]$t2),
  D = list(alpha=1, sig2=0.5, 
                 beta_intercept=linear.starts$beta_intercept, 
                 beta_slope=linear.starts$beta_slope,
                 k=length(EVtrees.bayou[[1]]$sb), ntheta=ncat-1, 
                 theta= linear.starts$beta_intercept + linear.starts$beta_slope*midbins,
                 sb=EVtrees.bayou[[1]]$sb, loc=EVtrees.bayou[[1]]$loc, t2=EVtrees.bayou[[1]]$t2)
)
plot(midbins, startpar.linearEVD$EV$theta)

```

```{r}
## Set priors
EV.fixed <- list(k=length(EVtrees.bayou[[1]]$sb), ntheta=ncat-1, sb=EVtrees.bayou[[1]]$sb, loc=EVtrees.bayou[[1]]$loc, t2=EVtrees.bayou[[1]]$t2)
D.fixed <- list(k=length(Dtrees.bayou[[1]]$sb), ntheta=ncat-1, sb=Dtrees.bayou[[1]]$sb, loc=Dtrees.bayou[[1]]$loc, t2=Dtrees.bayou[[1]]$t2)
prior.stepEVD <- list(
  EV = make.prior(tdEV$phy, plot.prior = FALSE, 
                          dists=list(dalpha="dlnorm", dsig2="dhalfcauchy", 
                                     dbeta_left="dunif",
                                     dbeta_right="dunif",
                                     dbeta_center="dunif",
                                     dsb="fixed", dk="fixed", 
                                     dtheta="dnull", dloc="fixed"), 
                          param=list(dalpha=list(meanlog=1, sdlog=1), dsig2=list(scale=0.1), 
                                     dbeta_left=list(min=-12, max=0),
                                     dbeta_right=list(min=-12, max=0),
                                     dbeta_center=list(min=-100, max=100),
                                     dk="fixed",
                                     dsb="fixed", 
                                     dtheta=list(), dloc="fixed"),
                          fixed=EV.fixed),
  D = make.prior(tdD$phy, plot.prior = FALSE, 
                          dists=list(dalpha="dlnorm", dsig2="dhalfcauchy", 
                                     dbeta_left="dunif",
                                     dbeta_right="dunif",
                                     dbeta_center="dunif",
                                     dsb="fixed", dk="fixed", 
                                     dtheta="dnull", dloc="fixed"), 
                          param=list(dalpha=list(meanlog=1, sdlog=1), dsig2=list(scale=0.1), 
                                     dbeta_left=list(min=-12, max=0),
                                     dbeta_right=list(min=-12, max=0),
                                     dbeta_center=list(min=-100, max=100),
                                     dk="fixed",
                                     dsb="fixed", 
                                     dtheta=list(), dloc="fixed"),
                          fixed=D.fixed)
)
  

prior.sigmoidEVD <- list(
  EV = make.prior(tdEV$phy, plot.prior = FALSE, 
                          dists=list(dalpha="dlnorm", dsig2="dhalfcauchy", 
                                     dbeta_left="dunif",
                                     dbeta_right="dunif",
                                     dbeta_center="dunif",
                                     dbeta_slope="dlnorm",
                                     dsb="fixed", dk="fixed", 
                                     dtheta="dnull", dloc="fixed"), 
                          param=list(dalpha=list(meanlog=1, sdlog=1), dsig2=list(scale=0.1), 
                                     dbeta_left=list(min=-12, max=0),
                                     dbeta_right=list(min=-12, max=0),
                                     dbeta_center=list(min=-100, max=100),
                                     dbeta_slope=list(meanlog=-3, sdlog=1),
                                     dk="fixed",
                                     dsb="fixed", 
                                     dtheta=list(), dloc="fixed"),
                          fixed=EV.fixed),
  D = make.prior(tdD$phy, plot.prior = FALSE, 
                          dists=list(dalpha="dlnorm", dsig2="dhalfcauchy", 
                                     dbeta_left="dunif",
                                     dbeta_right="dunif",
                                     dbeta_center="dunif",
                                     dbeta_slope="dlnorm",
                                     dsb="fixed", dk="fixed", 
                                     dtheta="dnull", dloc="fixed"), 
                          param=list(dalpha=list(meanlog=1, sdlog=1), dsig2=list(scale=0.1), 
                                     dbeta_left=list(min=-12, max=0),
                                     dbeta_right=list(min=-12, max=0),
                                     dbeta_center=list(min=-100, max=100),
                                     dbeta_slope=list(meanlog=-3, sdlog=1),
                                     dk="fixed",
                                     dsb="fixed", 
                                     dtheta=list(), dloc="fixed"),
                          fixed=D.fixed)
)

prior.linearEVD <- list(
  EV = make.prior(tdEV$phy, plot.prior = FALSE, 
                          dists=list(dalpha="dlnorm", dsig2="dhalfcauchy", 
                                     dbeta_intercept="dnorm",
                                     dbeta_slope="dnorm",
                                     dsb="fixed", dk="fixed", 
                                     dtheta="dnull", dloc="fixed"), 
                          param=list(dalpha=list(meanlog=1, sdlog=1), dsig2=list(scale=0.1), 
                                     dbeta_intercept=list(mean=-6, sd=1.5),
                                     dbeta_slope=list(mean=0, sd=0.1),
                                     dk="fixed",
                                     dsb="fixed", 
                                     dtheta=list(), dloc="fixed"),
                          fixed=EV.fixed),
  D = make.prior(tdD$phy, plot.prior = FALSE, 
                          dists=list(dalpha="dlnorm", dsig2="dhalfcauchy", 
                                     dbeta_intercept="dnorm",
                                     dbeta_slope="dnorm",
                                     dsb="fixed", dk="fixed", 
                                     dtheta="dnull", dloc="fixed"), 
                          param=list(dalpha=list(meanlog=1, sdlog=1), dsig2=list(scale=0.1), 
                                     dbeta_intercept=list(mean=-6, sd=1.5),
                                     dbeta_slope=list(mean=0, sd=0.1),
                                     dk="fixed",
                                     dsb="fixed", 
                                     dtheta=list(), dloc="fixed"),
                          fixed=D.fixed)
)
```


```{r}
mymcmcEV.step <- bayou.makeMCMC(tdEV$phy, tdEV[[trait]], pred=tdEV['binpred'], SE=0.02, model=model.stepTheta, prior=prior.stepEVD$EV, startpar=startpar.stepEVD$EV, new.dir=TRUE, outname="stepThetaEV_r1", plot.freq=NULL, ticker.freq=1000, samp = 100)
mymcmcD.step <- bayou.makeMCMC(tdD$phy, tdD[[trait]], pred=tdD['binpred'], SE=0.02, model=model.stepTheta, prior=prior.stepEVD$D, startpar=startpar.stepEVD$D, new.dir=TRUE, outname="stepThetaD_r1", plot.freq=NULL, ticker.freq=1000, samp = 100)

mymcmcEV.sigmoid <- bayou.makeMCMC(tdEV$phy, tdEV[[trait]], pred=tdEV['binpred'], SE=0.02, model=model.sigmoidTheta, prior=prior.sigmoidEVD$EV, startpar=startpar.sigmoidEVD$EV, new.dir=TRUE, outname="sigmoidThetaEV_r1", plot.freq=NULL, ticker.freq=1000, samp = 100)
mymcmcD.sigmoid <- bayou.makeMCMC(tdD$phy, tdD[[trait]], pred=tdD['binpred'], SE=0.02, model=model.sigmoidTheta, prior=prior.sigmoidEVD$D, startpar=startpar.sigmoidEVD$D, new.dir=TRUE, outname="sigmoidThetaD_r1", plot.freq=NULL, ticker.freq=1000, samp = 100)

mymcmcEV.linear <- bayou.makeMCMC(tdEV$phy, tdEV[[trait]], pred=tdEV['binpred'], SE=0.02, model=model.linearTheta, prior=prior.linearEVD$EV, startpar=startpar.linearEVD$EV, new.dir=TRUE, outname="linearThetaEV_r1", plot.freq=NULL, ticker.freq=1000, samp = 100)
mymcmcD.linear <- bayou.makeMCMC(tdD$phy, tdD[[trait]], pred=tdD['binpred'], SE=0.02, model=model.linearTheta, prior=prior.linearEVD$D, startpar=startpar.linearEVD$D, new.dir=TRUE, outname="linearThetaD_r1", plot.freq=NULL, ticker.freq=1000, samp = 100)

```


```{r}
strees.bayou <- EVtrees.bayou
mymcmcEV.step$run(100000)
chainEV.step <- mymcmcEV.step$load()
chainEV.step <- set.burnin(chainEV.step, 0.3)
plot(chainEV.step)

strees.bayou <- Dtrees.bayou
mymcmcD.step$run(100000)
chainD.step <- mymcmcD.step$load()
chainD.step <- set.burnin(chainD.step, 0.3)
plot(chainD.step)

```

```{r}
strees.bayou <- EVtrees.bayou
mymcmcEV.sigmoid$run(100000)
chainEV.sigmoid <- mymcmcEV.sigmoid$load()
chainEV.sigmoid <- set.burnin(chainEV.sigmoid, 0.3)
plot(chainEV.sigmoid)

strees.bayou <- Dtrees.bayou
mymcmcD.sigmoid$run(100000)
chainD.sigmoid <- mymcmcD.sigmoid$load()
chainD.sigmoid <- set.burnin(chainD.sigmoid, 0.3)
plot(chainD.sigmoid)
```



```{r}
strees.bayou <- EVtrees.bayou
mymcmcEV.linear$run(100000)
chainEV.linear <- mymcmcEV.linear$load()
chainEV.linear <- set.burnin(chainEV.linear, 0.3)
plot(chainEV.linear)

strees.bayou <- Dtrees.bayou
mymcmcD.linear$run(100000)
chainD.linear <- mymcmcD.linear$load()
chainD.linear <- set.burnin(chainD.linear, 0.3)
plot(chainD.linear)
```

```{r}
optimizeModelsOverSimmap <- function(i, strees.bayou=strees.bayou, chain.sigmoid=chain.sigmoid, chain.linear=chain.linear, chain.step=chain.step, cache = cache){
  sb <- strees.bayou[[i]]$sb
  t2 <- strees.bayou[[i]]$t2
  loc <- strees.bayou[[i]]$loc
  k <- length(sb)
  ntheta <- ncat - 1
lik.sigmoid <- function(pp){
  pars <- list(alpha = pp[1],
               sig2 = pp[2], 
               beta_left = pp[3],
               beta_right = pp[4], 
               beta_center = pp[5],
               beta_slope = pp[6],
               k = k, 
               ntheta=ntheta,
               theta = pp[3] + pp[4]/(1 + exp(pp[6] * (midbins - pp[5]))),
               sb = sb, 
               loc=loc,
               t2=t2)
  model.sigmoidTheta$lik.fn(pars, cache, cache$dat)$loglik[,1]
}

lik.sigmoid0 <- function(pp){
  pars <- list(alpha = pp[1],
               sig2 = pp[2], 
               beta_left = pp[3],
               beta_right = pp[4], 
               beta_center = 0,
               beta_slope = pp[5],
               k = k, 
               ntheta=ntheta,
               theta = pp[3] + pp[4]/(1 + exp(pp[5] * (midbins - 0))),
               sb = sb, 
               loc=loc,
               t2=t2)
  model.sigmoidTheta$lik.fn(pars, cache, cache$dat)$loglik[,1]
}

lik.tanhLin <- function(pp){
  pars <- list(alpha = pp[1],
               sig2 = pp[2], 
               beta_left = pp[3],
               beta_right = pp[4], 
               beta_center = pp[5],
               beta_slope = pp[6],
               k = k, 
               ntheta=ntheta,
               theta = pp[3] + pp[6]*midbins + pp[4]*tanh(midbins - pp[5]),
               sb = sb, 
               loc=loc,
               t2=t2)
  model.sigmoidTheta$lik.fn(pars, cache, cache$dat)$loglik[,1]
}

## Parameter order: alpha, sig2, beta_left, beta_right, beta_center, beta_slope
pars.sigmoid <- pull.pars(length(chain.sigmoid$gen), chain.sigmoid, model.sigmoidTheta)
pp.sigmoid <- unname(c(pars.sigmoid$alpha, pars.sigmoid$sig2, pars.sigmoid$beta_left, pars.sigmoid$beta_right, pars.sigmoid$beta_center, pars.sigmoid$beta_slope))

pp.sigmoid0 <- pp.sigmoid[-5]

lik.step <- function(pp){
  pars <- list(alpha = pp[1],
               sig2 = pp[2], 
               beta_left = pp[3],
               beta_right = pp[4], 
               beta_center = pp[5],
               k = k, 
               ntheta=ntheta,
               theta = ifelse(midbins>pp[5], pp[4], pp[3]),
               sb = sb, 
               loc=loc,
               t2=t2)
  model.stepTheta$lik.fn(pars, cache, cache$dat)$loglik[,1]
}

lik.step0 <- function(pp){
  pars <- list(alpha = pp[1],
               sig2 = pp[2], 
               beta_left = pp[3],
               beta_right = pp[4], 
               beta_center = 0,
               k = k, 
               ntheta=ntheta,
               theta = ifelse(midbins>0, pp[4], pp[3]),
               sb = sb, 
               loc=loc,
               t2=t2)
  model.stepTheta$lik.fn(pars, cache, cache$dat)$loglik[,1]
}
## Parameter order: alpha, sig2, beta_left, beta_right, beta_center, beta_slope
pars.step <- pull.pars(length(chain.step$gen), chain.step, model.stepTheta)
pp.step <- unname(c(pars.step$alpha, pars.step$sig2, pars.step$beta_left, pars.step$beta_right, pars.step$beta_center))
pp.step0 <- pp.step[-5]

lik.linear <- function(pp){
  pars <- list(alpha = pp[1],
               sig2 = pp[2], 
               beta_intercept = pp[3],
               beta_slope = pp[4], 
               k = k, 
               ntheta=ntheta,
               theta = pp[3] + pp[4]*midbins,
               sb = sb, 
               loc=loc,
               t2=t2)
  model.linearTheta$lik.fn(pars, cache, cache$dat)$loglik[,1]
}
## Parameter order: alpha, sig2, beta_left, beta_right, beta_center, beta_slope
pars.linear <- pull.pars(length(chain.linear$gen), chain.linear, model.linearTheta)
pp.linear <- unname(c(pars.linear$alpha, pars.linear$sig2, pars.linear$beta_intercept, pars.linear$beta_slope))

pp.tanhLin <- unname(c(pars.linear$alpha, pars.linear$sig2, pars.linear$beta_intercept, rnorm(1), runif(1, -10, 10), pars.linear$beta_slope))

opt.sigmoid <- optimx(pp.sigmoid, lik.sigmoid, control=list(maximize=TRUE), method=c('nlminb'), lower=c(0,0,-12,-12,-200, -0.1), 
                      upper=c(2000, 2000, 0, 0, 200, 10))
opt.step <- optimx(pp.step, lik.step, control=list(maximize=TRUE), method=c('nlminb'), lower=c(0,0,-12,-12,-200), 
                      upper=c(2000, 2000, 0, 0, 200))
opt.sigmoid0 <- optimx(pp.sigmoid0, lik.sigmoid0, control=list(maximize=TRUE), method=c('nlminb'), lower=c(0,0,-12,-12,-0.1), 
                      upper=c(2000, 2000, 0, 0, 10))
opt.step0 <- optimx(pp.step0, lik.step0, control=list(maximize=TRUE), method=c('nlminb'), lower=c(0,0,-12,-12), 
                      upper=c(2000, 2000, 0, 0))
opt.linear <- optimx(pp.linear, lik.linear, control=list(maximize=TRUE), method=c('nlminb'), lower=c(0,0,-12,-1), 
                      upper=c(2000, 2000, 0, 1))

opt.tanhLin <- optimx(pp.tanhLin, lik.tanhLin, control=list(maximize=TRUE), method=c('nlminb'), lower=c(0,0,-12, -5,-200, -1), upper=c(2000, 2000, 0, 5, 200, 1))

aics <- list()
aics[['aic.sigmoid']] <- 2*6 - 2*opt.sigmoid$value
aics[['aic.step']] <- 2*5 - 2*opt.step$value
aics[['aic.linear']] <- 2*4 - 2*opt.linear$value
aics[['aic.sigmoid0']] <- 2*5 - 2*opt.sigmoid0$value
aics[['aic.step0']] <- 2*4 - 2*opt.step0$value
aics[['aic.tanhLin']] <- 2*6 - 2*opt.tanhLin$value
aics

return(list(opt.sigmoid=opt.sigmoid, opt.linear=opt.linear, opt.step=opt.step, opt.sigmoid0=opt.sigmoid0, opt.step0=opt.step0, opt.tanhLin=opt.tanhLin, aics=aics))
}
```


```{r}
require(optimx)
cacheEV <- bayou:::.prepare.ou.univariate(tdEV$phy, tdEV[['lnVs']], SE=0.02, pred=tdEV['binpred'])
cacheD <- bayou:::.prepare.ou.univariate(tdD$phy, tdD[['lnVs']], SE=0.02, pred=tdD['binpred'])
EVopt.allmodels <- lapply(1:100, optimizeModelsOverSimmap, strees.bayou=EVtrees.bayou, chain.sigmoid = chainEV.sigmoid, chain.step=chainEV.step, chain.linear=chainEV.linear, cache = cacheEV)
Dopt.allmodels <- lapply(1:100, optimizeModelsOverSimmap, strees.bayou=Dtrees.bayou, chain.sigmoid = chainD.sigmoid, chain.step=chainD.step, chain.linear=chainD.linear, cache = cacheD)

```

```{r}
summarizeAllOpt <- function(opt.allmodels, cutoff=-Inf){
  all.sigmoid <- do.call(rbind, lapply(opt.allmodels, function(x) x$opt.sigmoid))
  all.step <- do.call(rbind, lapply(opt.allmodels, function(x) x$opt.step))
  all.sigmoid0 <- do.call(rbind, lapply(opt.allmodels, function(x) x$opt.sigmoid0))
  all.step0 <- do.call(rbind, lapply(opt.allmodels, function(x) x$opt.step0))
  all.linear <- do.call(rbind, lapply(opt.allmodels, function(x) x$opt.linear))
  all.tanhLin <- do.call(rbind, lapply(opt.allmodels, function(x) x$opt.tanhLin))
  all.aics <- do.call(rbind, lapply(opt.allmodels, function(x) do.call(cbind, x$aics)))
  all.aics[all.aics < cutoff] <- NA
  boxplot(all.aics)
  all.aics <- as.data.frame(all.aics)
  t.test(all.aics$aic.sigmoid, all.aics$aic.linear, paired = TRUE)
  hist(all.aics$aic.linear - all.aics$aic.sigmoid)
  tmp <- reshape2::melt(all.aics)
  aicws <- aicw(tmp$value)$w
  aicws <- round(tapply(aicws, tmp$variable, sum),2)
  return(list(sigmoid=all.sigmoid, step=all.step, sigmoid0=all.sigmoid0, step0 = all.step0, linear=all.linear, tanhLin=all.tanhLin, aics=all.aics, aicws=aicws))
}
sumEV <- summarizeAllOpt(EVopt.allmodels)
sumD <- summarizeAllOpt(Dopt.allmodels)

```

```{r}
sumEV$aicws
sumD$aicws
```

```{r}
dAICs <- apply(sumEV$aics, 1, function(x) x-min(x, na.rm=TRUE))
par(mar=c(10, 4, 1,1))
boxplot(t(dAICs), las=2, xlab="Model", ylab="dAIC", main="EV")
boxplot(t(dAICs), las=2, xlab="Model", ylab="dAIC", main="EV", ylim=c(0,50))
cat("Mean Evergreen dAIC values\n")
apply(t(dAICs), 2, mean)

```
```{r}
dAICs <- apply(sumD$aics, 1, function(x) x-min(x, na.rm=TRUE))
par(mar=c(10, 4, 1,1))
boxplot(t(dAICs), las=2, xlab="Model", ylab="dAIC", main="D")
boxplot(t(dAICs), las=2, xlab="Model", ylab="dAIC",main="D", ylim=c(0,8))
cat("Mean Deciduous dAIC values\n")
apply(t(dAICs), 2, mean)

```


```{r}
plot(td[['tmin.01']], td[['lnVs']], bg=c("tan","green")[as.numeric(td[['phenology']])], main="Temperature vs. vessel size", pch=21)
x <- -600:200
abline(v=0)

dum <- lapply(1:nrow(sumEV$sigmoid), function(i) lines(x, sumEV$sigmoid[i, 3] + sumEV$sigmoid[i, 4]/(1 + exp(sumEV$sigmoid[i, 6]*(x-sumEV$sigmoid[i, 5]))), col="green", lty=1))
dum <- lapply(1:nrow(sumEV$sigmoid0), function(i) lines(x, sumEV$sigmoid0[i, 3] + sumEV$sigmoid0[i, 4]/(1 + exp(sumEV$sigmoid0[i, 5]*(x-0))), col="limegreen", lty=1))

#dum <- lapply(1:nrow(sumEV$tanhLin), function(i) lines(x, sumEV$tanhLin[i, 3] + sumEV$tanhLin[i,6]*x + sumEV$tanhLin[i,4]*tanh(x-sumEV$tanhLin[i,5]), col="green", lty=1))

dum <- lapply(1:nrow(sumD$linear), function(i) lines(x, sumD$linear[i,3] + sumD$linear[i,4]*x, col="orange2", lty=1))
dum <- lapply(1:nrow(sumD$sigmoid0), function(i) lines(x, sumD$sigmoid0[i, 3] + sumD$sigmoid0[i, 4]/(1 + exp(sumD$sigmoid0[i, 5]*(x-0))), col="orange", lty=1))


#dum <- lapply(1:nrow(sumD$sigmoid0), function(i) lines(x, sumD$sigmoid0[i, 3] + sumD$sigmoid0[i,4]/(1+exp(sumD$sigmoid0[i,5]*x)), col="tan", lty=1))



```

```{r}
plot(td[['tmin.01']], td[['lnVs']], bg=c("tan","green")[as.numeric(td[['phenology']])], main="Temperature vs. vessel size", pch=21, xlab = "Temp (Cx10)", ylab="Log Vessel Diameter")
x <- -400:200
abline(v=0)

dum <- lapply(seq(1, length(chainEV.sigmoid$gen), 1), function(i) lines(x, chainEV.sigmoid$beta_left[i]+chainEV.sigmoid$beta_right[i]/(1 + exp(chainEV.sigmoid$beta_slope[i]*(x-chainEV.sigmoid$beta_center[i]))), col=makeTransparent(rgb(0, 1, 0), alpha=10)))

dum <- lapply(seq(1, length(chainD.linear$gen), 1), function(i) lines(x, chainD.linear$beta_intercept[i]+chainD.linear$beta_slope[i]*x, col=makeTransparent(rgb(0.9, 0.5, 0.2), alpha=10)))

```

